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Data-Association-Free Landmark-based SLAM
|
https://ieeexplore.ieee.org/document/10160719/
|
[
"Yihao Zhang",
"Odin A. Severinsen",
"John J. Leonard",
"Luca Carlone",
"Kasra Khosoussi",
"Yihao Zhang",
"Odin A. Severinsen",
"John J. Leonard",
"Luca Carlone",
"Kasra Khosoussi"
] |
We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the positions of an unknown number of landmarks in the environment, and potential data associations between measurements and landmarks. This setup is interesting since: (i) it arises when recovering from data association failu...
|
Efficient Bundle Adjustment for Coplanar Points and Lines
|
https://ieeexplore.ieee.org/document/10160834/
|
[
"Lipu Zhou",
"Jiacheng Liu",
"Fengguang Zhai",
"Pan Ai",
"Kefei Ren",
"Yinian Mao",
"Guoquan Huang",
"Ziyang Meng",
"Michael Kaess",
"Lipu Zhou",
"Jiacheng Liu",
"Fengguang Zhai",
"Pan Ai",
"Kefei Ren",
"Yinian Mao",
"Guoquan Huang",
"Ziyang Meng",
"Michael Kaess"
] |
Bundle adjustment (BA) is a well-studied fundamental problem in the robotics and vision community. In man-made environments, coplanar points and lines are ubiquitous. However, the number of works on bundle adjustment with coplanar points and lines is relatively small. This paper focuses on this special BA problem, referred to as $\pi-\mathbf{BA}$. For a point or a line on a plane, we derive a new ...
|
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
|
https://ieeexplore.ieee.org/document/10161360/
|
[
"Joey Wilson",
"Yuewei Fu",
"Arthur Zhang",
"Jingyu Song",
"Andrew Capodieci",
"Paramsothy Jayakumar",
"Kira Barton",
"Maani Ghaffari",
"Joey Wilson",
"Yuewei Fu",
"Arthur Zhang",
"Jingyu Song",
"Andrew Capodieci",
"Paramsothy Jayakumar",
"Kira Barton",
"Maani Ghaffari"
] |
Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (Con-vBKI) layer which learns to perform explicit Bayesian inference within a depthwise separable convolution...
|
SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
|
https://ieeexplore.ieee.org/document/10160907/
|
[
"Xingguang Zhong",
"Yue Pan",
"Jens Behley",
"Cyrill Stachniss",
"Xingguang Zhong",
"Yue Pan",
"Jens Behley",
"Cyrill Stachniss"
] |
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems. Challenges of traditional mapping methods include the balance between memory consumption and mapping accuracy. This paper addresses the problem of achieving large-scale 3D reconstruction using implicit representations built from 3D LiDAR measurements. We learn and store implicit features...
|
Efficient and Hybrid Decoder for Local Map Construction in Bird'-Eye-View
|
https://ieeexplore.ieee.org/document/10161331/
|
[
"Kun Tian",
"Yun Ye",
"Zheng Zhu",
"Peng Li",
"Guan Huang",
"Kun Tian",
"Yun Ye",
"Zheng Zhu",
"Peng Li",
"Guan Huang"
] |
High-definition maps are crucial perception elements for autonomous robot navigation systems, which can provide accurate scene layout and environment information for downstream motion prediction and planning control tasks. Traditional methods based on manual annotation or SLAM algorithms require massive labor efforts and time costs, which hinders the deployment of practical applications. Online co...
|
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
|
https://ieeexplore.ieee.org/document/10160337/
|
[
"Binqian Jiang",
"Shaojie Shen",
"Binqian Jiang",
"Shaojie Shen"
] |
This paper proposes Contour Context, a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation, targeting the urban autonomous driving scenario. We interpret the Cartesian bird's eye view (BEV) image projected from 3D LiDAR points as layered distribution of structures. To recover elevation information from BEVs, we slice them at diffe...
|
The Reflectance Field Map: Mapping Glass and Specular Surfaces in Dynamic Environments
|
https://ieeexplore.ieee.org/document/10161520/
|
[
"Paul Foster",
"Collin Johnson",
"Benjamin Kuipers",
"Paul Foster",
"Collin Johnson",
"Benjamin Kuipers"
] |
We present the Reflectance Field Map, a reliable real-time method for detecting shiny surfaces, like glass, metal, and mirrors, with lidar. The Reflectance Field Map combines the theory developed for Light Field Mapping, common in computer graphics, with occupancy grid mapping. Like early methods for sonar-based robot mapping, we show how the addition of angular viewpoint information to a standard...
|
Inverse Perspective Mapping-Based Neural Occupancy Grid Map for Visual Parking
|
https://ieeexplore.ieee.org/document/10160849/
|
[
"Xiangru Mu",
"Haoyang Ye",
"Daojun Zhu",
"Tongqing Chen",
"Tong Qin",
"Xiangru Mu",
"Haoyang Ye",
"Daojun Zhu",
"Tongqing Chen",
"Tong Qin"
] |
Sensing environmental obstacles and establishing an occupancy map of surroundings are critical to achieving automated parking for autonomous vehicles. This paper presents a method to obtain surrounding occupancy information from inverse perspective mapping (IPM) images. This method uses the easily-accessed pseudo-labels from LiDAR to supervise a visual network, which can detect occupied boundaries...
|
Efficient Implicit Neural Reconstruction Using LiDAR
|
https://ieeexplore.ieee.org/document/10160322/
|
[
"Dongyu Yan",
"Xiaoyang Lyu",
"Jieqi Shi",
"Yi Lin",
"Dongyu Yan",
"Xiaoyang Lyu",
"Jieqi Shi",
"Yi Lin"
] |
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage. Previous approaches have demonstrated impressive results using color or depth images but still have difficulty handling poor light conditions and large-scale scenes. Methods taking global point cloud as input require accurate registration and ground truth coordin...
|
Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station
|
https://ieeexplore.ieee.org/document/10161522/
|
[
"Jonas Beuchert",
"Marco Camurri",
"Maurice Fallon",
"Jonas Beuchert",
"Marco Camurri",
"Maurice Fallon"
] |
Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses ...
|
EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics
|
https://ieeexplore.ieee.org/document/10160948/
|
[
"Qinjie Lin",
"Guo Ye",
"Han Liu",
"Qinjie Lin",
"Guo Ye",
"Han Liu"
] |
We propose EMS®, a cloud-enabled massive computational experiment management system supporting high-throughput computational robotics research. Compared to existing systems, EMS® features a sky-based pipeline orchestrator which allows us to exploit heterogeneous computing environments painlessly (e.g., on-premise clusters, public clouds, edge devices) to optimally deploy large-scale computational ...
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Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ray Tracing for Embedded Hardware on Mobile Robots
|
https://ieeexplore.ieee.org/document/10161388/
|
[
"Alexander Mock",
"Thomas Wiemann",
"Joachim Hertzberg",
"Alexander Mock",
"Thomas Wiemann",
"Joachim Hertzberg"
] |
Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking. However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use trian...
|
A Framework for Fast Prototyping of Photo-realistic Environments with Multiple Pedestrians
|
https://ieeexplore.ieee.org/document/10160586/
|
[
"Sara Casao",
"Andrés Otero",
"Álvaro Serra-Gómez",
"Ana C. Murillo",
"Javier Alonso-Mora",
"Eduardo Montijano",
"Sara Casao",
"Andrés Otero",
"Álvaro Serra-Gómez",
"Ana C. Murillo",
"Javier Alonso-Mora",
"Eduardo Montijano"
] |
Robotic applications involving people often require advanced perception systems to better understand complex real-world scenarios. To address this challenge, photo-realistic and physics simulators are gaining popularity as a means of generating accurate data labeling and designing scenarios for evaluating generalization capabilities, e.g., lighting changes, camera movements or different weather co...
|
RoboSC: a domain-specific language for supervisory controller synthesis of ROS applications
|
https://ieeexplore.ieee.org/document/10161436/
|
[
"Bart Wesselink",
"Koen de Vos",
"Ivan Kuertev",
"Michel Reniers",
"Elena Torta",
"Bart Wesselink",
"Koen de Vos",
"Ivan Kuertev",
"Michel Reniers",
"Elena Torta"
] |
The paper presents a novel domain-specific language, RoboSC, for developing supervisory controllers for robotic applications. RoboSC supports concepts of ROS/ROS2 and supervisory control theory. It enables users to focus on the modeling and the synthesis process of supervisory controllers for ROS applications only because it generates all artifacts needed to connect such controllers to ROS applica...
|
KubeROS: A Unified Platform for Automated and Scalable Deployment of ROS2-based Multi-Robot Applications
|
https://ieeexplore.ieee.org/document/10160632/
|
[
"Yongzhou Zhang",
"Christian Wurll",
"Björn Hein",
"Yongzhou Zhang",
"Christian Wurll",
"Björn Hein"
] |
As advanced algorithms enable robots to handle more challenging tasks and operate more autonomously, the on-board computer cannot meet the increased demands regarding computing power and memory storage in an efficient way. Leveraging the massive computing power of the cloud and low-latency connectivity to the edge can compensate for this lack of computing resources. However, this introduces a new ...
|
Domain-specific languages for kinematic chains and their solver algorithms: lessons learned for composable models
|
https://ieeexplore.ieee.org/document/10160474/
|
[
"Sven Schneider",
"Nico Hochgeschwender",
"Herman Bruyninckx",
"Sven Schneider",
"Nico Hochgeschwender",
"Herman Bruyninckx"
] |
The Unified Robot Description Format (URDF) and, to a lesser extent, the COLLAborative Design Activity (COLLADA) format are two of the most popular domain-specific languages (DSLs) to represent kinematic chains in robotics with support in many tools including Gazebo, MoveIt!, KDL or IKFast. In this paper we analyse both DSLs with respect to their structure and semantics as seen by tools that produ...
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SIERRA: A Modular Framework for Accelerating Research and Improving Reproducibility
|
https://ieeexplore.ieee.org/document/10161279/
|
[
"John Harwell",
"Maria Gini",
"John Harwell",
"Maria Gini"
] |
We present SIERRA, a novel framework for accelerating development and improving reproducibility of results in robotics research. SIERRA accelerates research by automating the process of generating experiments from queries over independent variables, executing experiments, and processing the results to generate deliverables such as graphs and videos. It shifts the paradigm for testing hypotheses fr...
|
OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control
|
https://ieeexplore.ieee.org/document/10161272/
|
[
"Christopher E. Mower",
"João Moura",
"Nazanin Zamani Behabadi",
"Sethu Vijayakumar",
"Tom Vercauteren",
"Christos Bergeles",
"Christopher E. Mower",
"João Moura",
"Nazanin Zamani Behabadi",
"Sethu Vijayakumar",
"Tom Vercauteren",
"Christos Bergeles"
] |
This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC are increasingly receiving interest in optimal control and in particular handling dynamic environments. While a flurry of software libraries exists to handle such problems, they either provide interfaces that are limited to a specific probl...
|
CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network
|
https://ieeexplore.ieee.org/document/10161481/
|
[
"Mingze Wei",
"Yaomin Huang",
"Zhiyuan Xu",
"Ning Liu",
"Zhengping Che",
"Xinyu Zhang",
"Chaomin Shen",
"Feifei Feng",
"Chun Shan",
"Jian Tang",
"Mingze Wei",
"Yaomin Huang",
"Zhiyuan Xu",
"Ning Liu",
"Zhengping Che",
"Xinyu Zhang",
"Chaomin Shen",
"Feifei Feng",
"Chun Shan",
"Jian Tang"
] |
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net, for grasping unknown objects in a cluttered environment by efficiently predicting multi-finger gra...
|
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation
|
https://ieeexplore.ieee.org/document/10160846/
|
[
"Chaitanya Mitash",
"Fan Wang",
"Shiyang Lu",
"Vikedo Terhuja",
"Tyler Garaas",
"Felipe Polido",
"Manikantan Nambi",
"Chaitanya Mitash",
"Fan Wang",
"Shiyang Lu",
"Vikedo Terhuja",
"Tyler Garaas",
"Felipe Polido",
"Manikantan Nambi"
] |
This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the i...
|
FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments
|
https://ieeexplore.ieee.org/document/10161143/
|
[
"Jishnu Jaykumar P",
"Yu-Wei Chao",
"Yu Xiang",
"Jishnu Jaykumar P",
"Yu-Wei Chao",
"Yu Xiang"
] |
We introduce the Few-Shot Object Learning (FEWSOL) dataset for object recognition with a few images per object. We captured 336 real-world objects with 9 RGB-D images per object from different views. Fewsol has object segmentation masks, poses, and attributes. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. We investigated (i) few-shot object cla...
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WorldGen: A Large Scale Generative Simulator
|
https://ieeexplore.ieee.org/document/10160861/
|
[
"Chahat Deep Singh",
"Riya Kumari",
"Cornelia Fermüller",
"Nitin J. Sanket",
"Yiannis Aloimonos",
"Chahat Deep Singh",
"Riya Kumari",
"Cornelia Fermüller",
"Nitin J. Sanket",
"Yiannis Aloimonos"
] |
In the era of deep learning, data is the critical determining factor in the performance of neural network models. Generating large datasets suffers from various challenges such as scalability, cost efficiency and photorealism. To avoid expensive and strenuous dataset collection and annotations, researchers have inclined towards computer-generated datasets. However, a lack of photorealism and a lim...
|
Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences
|
https://ieeexplore.ieee.org/document/10160500/
|
[
"Jeff Ford",
"Jordan Ford",
"Jeff Ford",
"Jordan Ford"
] |
As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour (Fig. 1) and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for Li-DAR range and attribute scan sequences including multiple-return range, si...
|
3D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision
|
https://ieeexplore.ieee.org/document/10160669/
|
[
"Markus Suchi",
"Bernhard Neuberger",
"Amanzhol Salykov",
"Jean-Baptiste Weibel",
"Timothy Patten",
"Markus Vincze",
"Markus Suchi",
"Bernhard Neuberger",
"Amanzhol Salykov",
"Jean-Baptiste Weibel",
"Timothy Patten",
"Markus Vincze"
] |
Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore safety of these algorithms. At present, publicly available tools for annotating data are scarce and...
|
METEOR: A Dense, Heterogeneous, and Unstructured Traffic Dataset with Rare Behaviors
|
https://ieeexplore.ieee.org/document/10161281/
|
[
"Rohan Chandra",
"Xijun Wang",
"Mridul Mahajan",
"Rahul Kala",
"Rishitha Palugulla",
"Chandrababu Naidu",
"Alok Jain",
"Dinesh Manocha",
"Rohan Chandra",
"Xijun Wang",
"Mridul Mahajan",
"Rahul Kala",
"Rishitha Palugulla",
"Chandrababu Naidu",
"Alok Jain",
"Dinesh Manocha"
] |
We present a new traffic dataset, Meteor, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios. Meteor consists of more than 1000 one-minute videos, over 2 million annotated frames with bounding boxes and GPS trajectories for 16 unique agent categories, and more than 13 million bounding boxes for traffic agents. Meteor is a dataset for rare and interesting, m...
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kollagen: A Collaborative SLAM Pose Graph Generator
|
https://ieeexplore.ieee.org/document/10160514/
|
[
"Roberto C. Sundin",
"David Umsonst",
"Roberto C. Sundin",
"David Umsonst"
] |
In this paper, we address the lack of datasets for – and the issue of reproducibility in – collaborative SLAM pose graph optimizers by providing a novel pose graph generator. Our pose graph generator, kollagen, is based on a random walk in a planar grid world, similar to the popular M3500 dataset for single agent SLAM. It is simple to use and the user can set several parameters, e.g., the number o...
|
AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors
|
https://ieeexplore.ieee.org/document/10161097/
|
[
"Hang Yu",
"Guido C. H. E de Croon",
"Christophe De Wagter",
"Hang Yu",
"Guido C. H. E de Croon",
"Christophe De Wagter"
] |
Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we propose AvoidBench, a benchmarking suite which can...
|
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning
|
https://ieeexplore.ieee.org/document/10160522/
|
[
"Chong Zhang",
"Lizhi Yang",
"Chong Zhang",
"Lizhi Yang"
] |
Terrain-aware locomotion has become an emerging topic in legged robotics. However, it is hard to generate diverse, challenging, and realistic unstructured terrains in simulation, which limits the way researchers evaluate their locomotion policies. In this paper, we prototype the generation of a terrain dataset via terrain authoring and active learning, and the learned samplers can stably generate ...
|
Train Offline, Test Online: A Real Robot Learning Benchmark
|
https://ieeexplore.ieee.org/document/10160594/
|
[
"Gaoyue Zhou",
"Victoria Dean",
"Mohan Kumar Srirama",
"Aravind Rajeswaran",
"Jyothish Pari",
"Kyle Hatch",
"Aryan Jain",
"Tianhe Yu",
"Pieter Abbeel",
"Lerrel Pinto",
"Chelsea Finn",
"Abhinav Gupta",
"Gaoyue Zhou",
"Victoria Dean",
"Mohan Kumar Srirama",
"Aravind Rajeswaran",
"Jyothish Pari",
"Kyle Hatch",
"Aryan Jain",
"Tianhe Yu",
"Pieter Abbeel",
"Lerrel Pinto",
"Chelsea Finn",
"Abhinav Gupta"
] |
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data. We take on these challenges via a new benchmark: Train Offline, Test Online (TOTO). TOTO provides remote users with access to shared robots for evaluating methods on common...
|
Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
|
https://ieeexplore.ieee.org/document/10160885/
|
[
"Se Hwan Jeon",
"Steve Heim",
"Charles Khazoom",
"Sangbae Kim",
"Se Hwan Jeon",
"Steve Heim",
"Charles Khazoom",
"Sangbae Kim"
] |
The main challenge in developing effective reinforcement learning (RL) pipelines is often the design and tuning the reward functions. Well-designed shaping reward can lead to significantly faster learning. Naively formulated rewards, however, can conflict with the desired behavior and result in overfitting or even erratic performance if not properly tuned. In theory, the broad class of potential b...
|
Household Clothing Set and Benchmarks for Characterising End-Effector Cloth Manipulation
|
https://ieeexplore.ieee.org/document/10161398/
|
[
"Angus B. Clark",
"Luke Cramphorn-Neal",
"Michal Rachowiecki",
"Austin Gregg-Smith",
"Angus B. Clark",
"Luke Cramphorn-Neal",
"Michal Rachowiecki",
"Austin Gregg-Smith"
] |
The highly varied and deformable structure of clothing presents a challenging task in the area of robot manipulation. Recent literature has shown an increasing interest in this field, however limited information exists on the influence of end-effector selection, instead focusing on the perception, modelling, and methodology in handling fabrics. Here, we present a benchmark set of household clothin...
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Parameter Optimization for Manipulator Motion Planning using a Novel Benchmark Set
|
https://ieeexplore.ieee.org/document/10160694/
|
[
"Carl Gaebert",
"Sascha Kaden",
"Benjamin Fischer",
"Ulrike Thomas",
"Carl Gaebert",
"Sascha Kaden",
"Benjamin Fischer",
"Ulrike Thomas"
] |
Sampling-based motion planning algorithms have been continuously developed for more than two decades. Apart from mobile robots, they are also widely used in manipulator motion planning. Hence, these methods play a key role in collaborative and shared workspaces. Despite numerous improvements, their performance can highly vary depending on the chosen parameter setting. The optimal parameters depend...
|
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
|
https://ieeexplore.ieee.org/document/10160583/
|
[
"Zifan Xu",
"Bo Liu",
"Xuesu Xiao",
"Anirudh Nair",
"Peter Stone",
"Zifan Xu",
"Bo Liu",
"Xuesu Xiao",
"Anirudh Nair",
"Peter Stone"
] |
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-based navigation systems. For example, most learning approaches lack safety guarantees; and learned navigation systems may not generalize well to unseen environments. Despite a variety of recent learning techniques to tackl...
|
A Benchmark for Multi-Robot Planning in Realistic, Complex and Cluttered Environments
|
https://ieeexplore.ieee.org/document/10161005/
|
[
"Simon Schaefer",
"Luigi Palmieri",
"Lukas Heuer",
"Ruediger Dillmann",
"Sven Koenig",
"Alexander Kleiner",
"Simon Schaefer",
"Luigi Palmieri",
"Lukas Heuer",
"Ruediger Dillmann",
"Sven Koenig",
"Alexander Kleiner"
] |
Several successful approaches exist for solving the complex problem of multi-robot planning and coordination. Due to the lack of adequate benchmarking tools, comparing these approaches and judging their suitability for use in realistic scenarios is currently difficult. Therefore, we propose an open-source benchmark suite that aims to close this gap. Unlike existing benchmarks, our approach uses fu...
|
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence
|
https://ieeexplore.ieee.org/document/10160484/
|
[
"Junhyung Lee",
"Junho Koh",
"Youngwoo Lee",
"Jun Won Choi",
"Junhyung Lee",
"Junho Koh",
"Youngwoo Lee",
"Jun Won Choi"
] |
LiDAR sensors are widely used for 3D object detection in various mobile robotics applications. LiDAR sensors continuously generate point cloud data in real-time. Conventional 3D object detectors detect objects using a set of points acquired over a fixed duration. However, recent studies have shown that the performance of object detection can be further enhanced by utilizing spatio-temporal informa...
|
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection
|
https://ieeexplore.ieee.org/document/10160489/
|
[
"Jingyu Li",
"Zhe Liu",
"Jinghua Hou",
"Dingkang Liang",
"Jingyu Li",
"Zhe Liu",
"Jinghua Hou",
"Dingkang Liang"
] |
In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision in...
|
Fast Staircase Detection and Estimation using 3D Point Clouds with Multi-detection Merging for Heterogeneous Robots
|
https://ieeexplore.ieee.org/document/10160258/
|
[
"Prasanna Sriganesh",
"Namya Bagree",
"Bhaskar Vundurthy",
"Matthew Travers",
"Prasanna Sriganesh",
"Namya Bagree",
"Bhaskar Vundurthy",
"Matthew Travers"
] |
Robotic systems need advanced mobility capabili-ties to operate in complex, three-dimensional environments designed for human use, e.g., multi-level buildings. Incorporating some level of autonomy enables robots to operate robustly, reliably, and efficiently in such complex environments, e.g., automatically “returning home” if communication between an operator and robot is lost during deployment. ...
|
Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detection
|
https://ieeexplore.ieee.org/document/10161165/
|
[
"Xiaofang Wang",
"Kris M. Kitani",
"Xiaofang Wang",
"Kris M. Kitani"
] |
Considerable research effort has been devoted to LiDAR-based 3D object detection and empirical performance has been significantly improved. While progress has been en-couraging, we observe an overlooked issue: it is not yet common practice to compare different 3D detectors under the same cost, e.g., inference latency. This makes it difficult to quantify the true performance gain brought by recentl...
|
Zero-shot Object Detection Based on Dynamic Semantic Vectors
|
https://ieeexplore.ieee.org/document/10160870/
|
[
"Haoyu Li",
"Jilin Mei",
"Jiancong Zhou",
"Yu Hu",
"Haoyu Li",
"Jilin Mei",
"Jiancong Zhou",
"Yu Hu"
] |
Zero-shot object detection has shown its ability to overcome the problems of data scarcity and novel classes. Existing methods generally utilize static semantic vectors to classify objects and guide the network to map visual features to semantic vectors. However, the distribution of semantic vectors cannot adequately represent visual features, which makes migration from seen to unseen classes diff...
|
Road Anomaly Segmentation Based on Pixel-wise Logit Variance with Iterative Background Highlighting
|
https://ieeexplore.ieee.org/document/10161159/
|
[
"Dongkun Lee",
"Han-Gyu Kim",
"Ho-Jin Choi",
"Dongkun Lee",
"Han-Gyu Kim",
"Ho-Jin Choi"
] |
Anomaly segmentation on the urban landscape scene is an important task in autonomous driving. This process exploits a pre-trained semantic segmentation network to estimate anomalous regions. Anomaly segmentation approaches implemented with extra requirements such as out-of-domain data, extra network, or network retraining might increase the computational cost or degradation of segmentation perform...
|
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation
|
https://ieeexplore.ieee.org/document/10160999/
|
[
"Namyup Kim",
"Taeyoung Son",
"Jaehyun Pahk",
"Cuiling Lan",
"Wenjun Zeng",
"Suha Kwak",
"Namyup Kim",
"Taeyoung Son",
"Jaehyun Pahk",
"Cuiling Lan",
"Wenjun Zeng",
"Suha Kwak"
] |
Domain generalization for semantic segmentation is highly demanded in real applications, where a trained model is expected to work well in previously unseen domains. One challenge lies in the lack of data which could cover the diverse distributions of the possible unseen domains for training. In this paper, we propose a WEb-image assisted Domain GEneralization (WEDGE) scheme, which is the first to...
|
Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach
|
https://ieeexplore.ieee.org/document/10160283/
|
[
"Tae-Min Choi",
"Jong-Hwan Kim",
"Tae-Min Choi",
"Jong-Hwan Kim"
] |
In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous iFSD works achieved the desired results by applying metalearning. However, meta-learning approaches show insufficient performance that is difficult to apply to practical problems. In this light, we propose a simple fine...
|
Discriminative 3D Shape Modeling for Few-Shot Instance Segmentation
|
https://ieeexplore.ieee.org/document/10160644/
|
[
"Anoop Cherian",
"Siddarth Jain",
"Tim K. Marks",
"Alan Sullivan",
"Anoop Cherian",
"Siddarth Jain",
"Tim K. Marks",
"Alan Sullivan"
] |
In this paper, we present a simple and efficient scheme for segmenting approximately convex 3D object instances in depth images in a few-shot setting via discriminatively modeling the 3D shape of the object using a neural network. Our key idea is to select pairs of 3D points on the depth image between which we compute surface geodesics. As the number of such geodesics is quadratic in the number of...
|
Multi-to-Single Knowledge Distillation for Point Cloud Semantic Segmentation
|
https://ieeexplore.ieee.org/document/10160496/
|
[
"Shoumeng Qiu",
"Feng Jiang",
"Haiqiang Zhang",
"Xiangyang Xue",
"Jian Pu",
"Shoumeng Qiu",
"Feng Jiang",
"Haiqiang Zhang",
"Xiangyang Xue",
"Jian Pu"
] |
3D point cloud semantic segmentation is one of the fundamental tasks for environmental understanding. Although significant progress has been made in recent years, the performance of classes with few examples or few points is still far from satisfactory. In this paper, we propose a novel multi-to-single knowledge distillation framework for the 3D point cloud semantic segmentation task to boost the ...
|
On Improving Boundary Quality of Instance Segmentation in Cluttered and Chaotic Scenarios
|
https://ieeexplore.ieee.org/document/10160261/
|
[
"Biqi Yang",
"Xiaojie Gao",
"Xianzhi Li",
"Yun-Hui Liu",
"Chi-Wing Fu",
"Pheng-Ann Heng",
"Biqi Yang",
"Xiaojie Gao",
"Xianzhi Li",
"Yun-Hui Liu",
"Chi-Wing Fu",
"Pheng-Ann Heng"
] |
Instance segmentation is a long-standing task for supporting robotic bin picking. However, objects of diverse classes can be closely packed with occlusions in cluttered and chaotic scenes, hence, even recent methods could have difficulty in locating clear and precise boundaries to distinguish nearby objects. In this work, we aim to improve the boundary quality of the instance masks for robust and ...
|
Real-time Background Subtraction under Varying Lighting Conditions
|
https://ieeexplore.ieee.org/document/10160223/
|
[
"Sisi Liang",
"Darren Baker",
"Sisi Liang",
"Darren Baker"
] |
Background subtraction is an important topic in computer vision and video analysis. It is challenging to robustly segment foreground and background in complex scenarios. In the literature there are efforts to address some of the main challenges such as illumination change, dynamic backgrounds, hard shadows, and intermittent object motion. However, most of the research has focused on applying advan...
|
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
|
https://ieeexplore.ieee.org/document/10160674/
|
[
"Jilin Mei",
"Junbao Zhou",
"Yu Hu",
"Jilin Mei",
"Junbao Zhou",
"Yu Hu"
] |
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the novel classes without considering the base classes. This setting cannot be direct...
|
ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals
|
https://ieeexplore.ieee.org/document/10160343/
|
[
"Shihong Fang",
"Haoran Zhu",
"Devansh Bisla",
"Anna Choromanska",
"Satish Ravindran",
"Dongyin Ren",
"Ryan Wu",
"Shihong Fang",
"Haoran Zhu",
"Devansh Bisla",
"Anna Choromanska",
"Satish Ravindran",
"Dongyin Ren",
"Ryan Wu"
] |
Among various sensors for assisted and autonomous driving systems, automotive radar has been considered as a robust and low-cost solution even in adverse weather or lighting conditions. With the recent development of radar technologies and open-sourced annotated data sets, semantic segmentation with radar signals has become very promising. However, existing methods are either computationally expen...
|
ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation
|
https://ieeexplore.ieee.org/document/10160410/
|
[
"Lingdong Kong",
"Niamul Quader",
"Venice Erin Liong",
"Lingdong Kong",
"Niamul Quader",
"Venice Erin Liong"
] |
Transferring knowledge learned from the labeled source domain to the raw target domain for unsupervised domain adaptation (UDA) is essential to the scalable deployment of autonomous driving systems. State-of-the-art methods in UDA often employ a key idea: utilizing joint supervision signals from both source and target domains for self-training. In this work, we improve and extend this aspect. We p...
|
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
|
https://ieeexplore.ieee.org/document/10160707/
|
[
"Darren Tsai",
"Julie Stephany Berrio",
"Mao Shan",
"Eduardo Nebot",
"Stewart Worrall",
"Darren Tsai",
"Julie Stephany Berrio",
"Mao Shan",
"Eduardo Nebot",
"Stewart Worrall"
] |
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and tested on another. We observe that lidar scan pattern differences form a large ...
|
nerf2nerf: Pairwise Registration of Neural Radiance Fields
|
https://ieeexplore.ieee.org/document/10160794/
|
[
"Lily Goli",
"Daniel Rebain",
"Sara Sabour",
"Animesh Garg",
"Andrea Tagliasacchi",
"Lily Goli",
"Daniel Rebain",
"Sara Sabour",
"Animesh Garg",
"Andrea Tagliasacchi"
] |
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (NeRF)-neural 3D scene representations trained from collections of calibrated images. NeRF does not decompose illumination and color, so to make registration invariant to illumination, we introduce the concept of a “surface...
|
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields
|
https://ieeexplore.ieee.org/document/10161544/
|
[
"Arunkumar Byravan",
"Jan Humplik",
"Leonard Hasenclever",
"Arthur Brussee",
"Francesco Nori",
"Tuomas Haarnoja",
"Ben Moran",
"Steven Bohez",
"Fereshteh Sadeghi",
"Bojan Vujatovic",
"Nicolas Heess",
"Arunkumar Byravan",
"Jan Humplik",
"Leonard Hasenclever",
"Arthur Brussee",
"Francesco Nori",
"Tuomas Haarnoja",
"Ben Moran",
"Steven Bohez",
"Fereshteh Sadeghi",
"Bojan Vujatovic",
"Nicolas Heess"
] |
We present a system for applying sim2real approaches to “in the wild” scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a short video of a static scene collected using a generic phone, we learn the scene's contact geometry and a function for novel view synthesis using a Neural Radiance Field (NeRF). We augment the NeRF rendering of the static s...
|
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields
|
https://ieeexplore.ieee.org/document/10161012/
|
[
"Niko Sünderhauf",
"Jad Abou-Chakra",
"Dimity Miller",
"Niko Sünderhauf",
"Jad Abou-Chakra",
"Dimity Miller"
] |
We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered. The naive ensembles investigated in prior work simply average rendered RGB images to quantify the model uncertainty caused by conflicting explanations of the observed scene. In contrast, we additionally consider the termination probabilitie...
|
Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation
|
https://ieeexplore.ieee.org/document/10161117/
|
[
"Yunzhi Lin",
"Thomas Müller",
"Jonathan Tremblay",
"Bowen Wen",
"Stephen Tyree",
"Alex Evans",
"Patricio A. Vela",
"Stan Birchfield",
"Yunzhi Lin",
"Thomas Müller",
"Jonathan Tremblay",
"Bowen Wen",
"Stephen Tyree",
"Alex Evans",
"Patricio A. Vela",
"Stan Birchfield"
] |
We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene. Given a single observed RGB image of the target, we can predict the translation and rotation of the camera by minimizing the residual between pixels rendered from a fast NeRF model and pixels in the observed image. We integrate a moment...
|
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
|
https://ieeexplore.ieee.org/document/10161420/
|
[
"Jianlin Liu",
"Qiang Nie",
"Yong Liu",
"Chengjie Wang",
"Jianlin Liu",
"Qiang Nie",
"Yong Liu",
"Chengjie Wang"
] |
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene representation in our pipeline, which supports continuous 3D descriptors generation and neural rendering. By unifying the feature matching and the scene coordinate regression to the same ...
|
Multimodal Neural Radiance Field
|
https://ieeexplore.ieee.org/document/10160388/
|
[
"Haidong Zhu",
"Yuyin Sun",
"Chi Liu",
"Lu Xia",
"Jiajia Luo",
"Nan Qiao",
"Ram Nevatia",
"Cheng–Hao Kuo",
"Haidong Zhu",
"Yuyin Sun",
"Chi Liu",
"Lu Xia",
"Jiajia Luo",
"Nan Qiao",
"Ram Nevatia",
"Cheng–Hao Kuo"
] |
This paper addresses the challenge of reconstructing a scene with a neural radiance field (NeRF) for robot vision and scene understanding using multiple modalities. Researchers have introduced the use of NeRF to represent an object for synthesizing and rendering novel views of complex scenes by optimizing a 3-D radiance field for ray casting and rendering for 2-D RGB images. However, using RGB ima...
|
Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping
|
https://ieeexplore.ieee.org/document/10160950/
|
[
"Chi-Ming Chung",
"Yang-Che Tseng",
"Ya-Ching Hsu",
"Xiang-Qian Shi",
"Yun-Hung Hua",
"Jia-Fong Yeh",
"Wen-Chin Chen",
"Yi-Ting Chen",
"Winston H. Hsu",
"Chi-Ming Chung",
"Yang-Che Tseng",
"Ya-Ching Hsu",
"Xiang-Qian Shi",
"Yun-Hung Hua",
"Jia-Fong Yeh",
"Wen-Chin Chen",
"Yi-Ting Chen",
"Winston H. Hsu"
] |
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated. To achieve this, we need a visual SLAM that easily adapts to new scenes without pre-training and generates dense maps for downstream tasks in real-time. None of the previous learning-based and non-learning-based visual SLAMs satisfy all needs due to the intrinsic limitations of their...
|
NeRFing it: Offline Object Segmentation Through Implicit Modeling
|
https://ieeexplore.ieee.org/document/10161040/
|
[
"Kenneth Blomqvist",
"Jen Jen Chung",
"Lionel Ott",
"Roland Siegwart",
"Kenneth Blomqvist",
"Jen Jen Chung",
"Lionel Ott",
"Roland Siegwart"
] |
Most recently proposed methods for robotic per-ception are based on deep learning, which require very large datasets to perform well. The accuracy of a learned model is mainly dependent on the data distribution it was trained on. Thus for deploying such models, it is crucial to use training data belonging to the robot's environment. However, collecting and labeling data is a significant bottleneck...
|
Using Learning Curve Predictions to Learn from Incorrect Feedback
|
https://ieeexplore.ieee.org/document/10161105/
|
[
"Taylor A. Kessler Faulkner",
"Andrea L. Thomaz",
"Taylor A. Kessler Faulkner",
"Andrea L. Thomaz"
] |
Robots can incorporate data from human teachers when learning new tasks. However, this data can often be noisy, which can cause robots to learn slowly or not at all. One method for learning from human teachers is Human-in-the-loop Reinforcement Learning (HRL), which can combine information from both an environmental reward and external feedback from human teachers. However, many HRL methods assume...
|
Conflict-constrained Multi-agent Reinforcement Learning Method for Parking Trajectory Planning
|
https://ieeexplore.ieee.org/document/10160698/
|
[
"Siyuan Chen",
"Meiling Wang",
"Yi Yang",
"Wenjie Song",
"Siyuan Chen",
"Meiling Wang",
"Yi Yang",
"Wenjie Song"
] |
Automated Valet Parking (AVP) has been exten-sively researched as an important application of autonomous driving. Considering the high dynamics and density of real parking lots, a system that considers multiple vehicles simultaneously is more robust and efficient than a single vehicle setting as in most studies. In this paper, we propose a dis-tributed Multi-agent Reinforcement Learning(MARL) meth...
|
Improving robot navigation in crowded environments using intrinsic rewards
|
https://ieeexplore.ieee.org/document/10160876/
|
[
"Diego Martinez-Baselga",
"Luis Riazuelo",
"Luis Montano",
"Diego Martinez-Baselga",
"Luis Riazuelo",
"Luis Montano"
] |
Autonomous navigation in crowded environments is an open problem with many applications, essential for the coexistence of robots and humans in the smart cities of the future. In recent years, deep reinforcement learning approaches have proven to outperform model-based algorithms. Nevertheless, even though the results provided are promising, the works are not able to take advantage of the capabilit...
|
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers
|
https://ieeexplore.ieee.org/document/10160684/
|
[
"Yan Wang",
"Gautham Vasan",
"A. Rupam Mahmood",
"Yan Wang",
"Gautham Vasan",
"A. Rupam Mahmood"
] |
Real-time learning is crucial for robotic agents adapting to ever-changing, non-stationary environments. A common setup for a robotic agent is to have two different computers simultaneously: a resource-limited local computer tethered to the robot and a powerful remote computer connected wirelessly. Given such a setup, it is unclear to what extent the performance of a learning system can be affecte...
|
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
|
https://ieeexplore.ieee.org/document/10160991/
|
[
"Desong Du",
"Shaohang Han",
"Naiming Qi",
"Haitham Bou Ammar",
"Jun Wang",
"Wei Pan",
"Desong Du",
"Shaohang Han",
"Naiming Qi",
"Haitham Bou Ammar",
"Jun Wang",
"Wei Pan"
] |
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a ...
|
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
|
https://ieeexplore.ieee.org/document/10161548/
|
[
"Puze Liu",
"Kuo Zhang",
"Davide Tateo",
"Snehal Jauhri",
"Zhiyuan Hu",
"Jan Peters",
"Georgia Chalvatzaki",
"Puze Liu",
"Kuo Zhang",
"Davide Tateo",
"Snehal Jauhri",
"Zhiyuan Hu",
"Jan Peters",
"Georgia Chalvatzaki"
] |
Safety is a fundamental property for the real-world deployment of robotic platforms. Any control policy should avoid dangerous actions that could harm the environment, humans, or the robot itself. In reinforcement learning (RL), safety is crucial when exploring a new environment to learn a new skill. This paper introduces a new formulation of safe exploration for robotic RL in the tangent space of...
|
Robotic Control Using Model Based Meta Adaption
|
https://ieeexplore.ieee.org/document/10160425/
|
[
"Karam Daaboul",
"Joel Ikels",
"J. Marius Zöllner",
"Karam Daaboul",
"Joel Ikels",
"J. Marius Zöllner"
] |
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning via world models with Meta Reinforcement Learning (MRL) for increased sample efficiency. However, adaption to unknown tasks does not always result in preferable agent behavior. This paper introduces a new Meta Adaptatio...
|
SACPlanner: Real-World Collision Avoidance with a Soft Actor Critic Local Planner and Polar State Representations
|
https://ieeexplore.ieee.org/document/10161129/
|
[
"Khaled Nakhleh",
"Minahil Raza",
"Mack Tang",
"Matthew Andrews",
"Rinu Boney",
"Ilija Hadžić",
"Jeongran Lee",
"Atefeh Mohajeri",
"Karina Palyutina",
"Khaled Nakhleh",
"Minahil Raza",
"Mack Tang",
"Matthew Andrews",
"Rinu Boney",
"Ilija Hadžić",
"Jeongran Lee",
"Atefeh Mohajeri",
"Karina Palyutina"
] |
We study the training performance of ROS local planners based on Reinforcement Learning (RL), and the trajectories they produce on real-world robots. We show that recent enhancements to the Soft Actor Critic (SAC) algorithm such as RAD and DrQ achieve almost perfect training after only 10000 episodes. We also observe that on real-world robots the resulting SACPlanner is more reactive to obstacles ...
|
Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation
|
https://ieeexplore.ieee.org/document/10160268/
|
[
"Xingyu Zhu",
"Xin Wang",
"Jonathan Freer",
"Hyung Jin Chang",
"Yixing Gao",
"Xingyu Zhu",
"Xin Wang",
"Jonathan Freer",
"Hyung Jin Chang",
"Yixing Gao"
] |
Clothes grasping and unfolding is a core step in robotic-assisted dressing. Most existing works leverage depth images of clothes to train a deep learning-based model to recognize suitable grasping points. These methods often utilize physics engines to synthesize depth images to reduce the cost of real labeled data collection. However, the natural domain gap between synthetic and real images often ...
|
Privacy-Preserving Video Conferencing via Thermal-Generative Images
|
https://ieeexplore.ieee.org/document/10161205/
|
[
"Sheng–Yang Chiu",
"Yu–Ting Huang",
"Chieh–Ting Lin",
"Yu–Chee Tseng",
"Jen–Jee Chen",
"Meng–Hsuan Tu",
"Bo–Chen Tung",
"YuJou Nieh",
"Sheng–Yang Chiu",
"Yu–Ting Huang",
"Chieh–Ting Lin",
"Yu–Chee Tseng",
"Jen–Jee Chen",
"Meng–Hsuan Tu",
"Bo–Chen Tung",
"YuJou Nieh"
] |
Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-resolution thermal images as conditions to...
|
Streaming LifeLong Learning With Any-Time Inference
|
https://ieeexplore.ieee.org/document/10160358/
|
[
"Soumya Banerjee",
"Vinay Kumar Verma",
"Vinay P. Namboodiri",
"Soumya Banerjee",
"Vinay Kumar Verma",
"Vinay P. Namboodiri"
] |
Despite rapid advancements in the lifelong learning (LL) research, a large body of research mainly focuses on improving the performance in the existing static continual learning (CL) setups. These methods lack the ability to succeed in a rapidly changing dynamic environment, where an AI agent needs to quickly learn new instances in a ‘single pass' from the non-i.i.d (also possibly temporally conti...
|
Code as Policies: Language Model Programs for Embodied Control
|
https://ieeexplore.ieee.org/document/10160591/
|
[
"Jacky Liang",
"Wenlong Huang",
"Fei Xia",
"Peng Xu",
"Karol Hausman",
"Brian Ichter",
"Pete Florence",
"Andy Zeng",
"Jacky Liang",
"Wenlong Huang",
"Fei Xia",
"Peng Xu",
"Karol Hausman",
"Brian Ichter",
"Pete Florence",
"Andy Zeng"
] |
Large language models (LLMs) trained on code-completion have been shown to be capable of synthesizing simple Python programs from docstrings [1]. We find that these code-writing LLMs can be re-purposed to write robot policy code, given natural language commands. Specifically, policy code can express functions or feedback loops that process perception outputs (e.g., from object detectors [2], [3]) ...
|
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
|
https://ieeexplore.ieee.org/document/10161477/
|
[
"Hoang-Giang Cao",
"Weihao Zeng",
"I-Chen Wu",
"Hoang-Giang Cao",
"Weihao Zeng",
"I-Chen Wu"
] |
It is crucial to address the following issues for ubiquitous robotics manipulation applications: (a) vision-based manipulation tasks require the robot to visually learn and understand the object with rich information like dense object descriptors; and (b) sim-to-real transfer in robotics aims to close the gap between simulated and real data. In this paper, we present Sim-to-Real Dense Object Nets ...
|
Learning Visual-Audio Representations for Voice-Controlled Robots
|
https://ieeexplore.ieee.org/document/10161461/
|
[
"Peixin Chang",
"Shuijing Liu",
"D. Livingston McPherson",
"Katherine Driggs-Campbell",
"Peixin Chang",
"Shuijing Liu",
"D. Livingston McPherson",
"Katherine Driggs-Campbell"
] |
Based on the recent advancements in representation learning, we propose a novel pipeline for task-oriented voice-controlled robots with raw sensor inputs. Previous methods rely on a large number of labels and task-specific reward functions. Not only can such an approach hardly be improved after the deployment, but also has limited generalization across robotic platforms and tasks. To address these...
|
Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations
|
https://ieeexplore.ieee.org/document/10160888/
|
[
"Negin Heravi",
"Ayzaan Wahid",
"Corey Lynch",
"Pete Florence",
"Travis Armstrong",
"Jonathan Tompson",
"Pierre Sermanet",
"Jeannette Bohg",
"Debidatta Dwibedi",
"Negin Heravi",
"Ayzaan Wahid",
"Corey Lynch",
"Pete Florence",
"Travis Armstrong",
"Jonathan Tompson",
"Pierre Sermanet",
"Jeannette Bohg",
"Debidatta Dwibedi"
] |
Perceptual understanding of the scene and the relationship between its different components is important for successful completion of robotic tasks. Representation learning has been shown to be a powerful technique for this, but most of the current methodologies learn task specific representations that do not necessarily transfer well to other tasks. Furthermore, representations learned by supervi...
|
Sample-Efficient Goal-Conditioned Reinforcement Learning via Predictive Information Bottleneck for Goal Representation Learning
|
https://ieeexplore.ieee.org/document/10161213/
|
[
"Qiming Zou",
"Einoshin Suzuki",
"Qiming Zou",
"Einoshin Suzuki"
] |
We propose Predictive Information bottleneck for Goal representation learning (PI-Goal), a self-supervised method for sample-efficient goal-conditioned reinforcement learning (RL). Goal-conditioned RL learns to reach commanded goals with reward signals. A goal could be given in a noisy or abstract form, and thus jeopardizes sample efficiency. Previous methods usually assume that the agent can map ...
|
Context-aware robot control using gesture episodes
|
https://ieeexplore.ieee.org/document/10161308/
|
[
"Petr Vanc",
"Jan Kristof Behrens",
"Karla Stepanova",
"Petr Vanc",
"Jan Kristof Behrens",
"Karla Stepanova"
] |
Collaborative robots became a popular tool for increasing productivity in partly automated manufacturing plants. Intuitive robot teaching methods are required to quickly and flexibly adapt the robot programs to new tasks. Gestures have an essential role in human communication. However, in human-robot-interaction scenarios, gesture-based user interfaces are so far used rarely, and if they employ a ...
|
Automated Action Evaluation for Robotic Imitation Learning via Siamese Neural Networks
|
https://ieeexplore.ieee.org/document/10161364/
|
[
"Xiang Chang",
"Fei Chao",
"Changjing Shang",
"Qiang Shen",
"Xiang Chang",
"Fei Chao",
"Changjing Shang",
"Qiang Shen"
] |
Despite recent advances in video-guided robotic imitation learning, many methods still rely on human experts to provide sparse rewards that indicate whether robots have successfully completed tasks. The challenge of enabling robots to autonomously evaluate whether their actions can complete complex, multi-stage tasks remains unresolved. In this work, we propose an efficient few-shot robotic learni...
|
Failure-aware Policy Learning for Self-assessable Robotics Tasks
|
https://ieeexplore.ieee.org/document/10160889/
|
[
"Kechun Xu",
"Runjian Chen",
"Shuqi Zhao",
"Zizhang Li",
"Hongxiang Yu",
"Ci Chen",
"Yue Wang",
"Rong Xiong",
"Kechun Xu",
"Runjian Chen",
"Shuqi Zhao",
"Zizhang Li",
"Hongxiang Yu",
"Ci Chen",
"Yue Wang",
"Rong Xiong"
] |
Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the selected action evaluated as failed by the self-assessment rules, and re-choose one with the next-highe...
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Multimodal Time Series Learning of Robots Based on Distributed and Integrated Modalities: Verification with a Simulator and Actual Robots
|
https://ieeexplore.ieee.org/document/10161223/
|
[
"Hideyuki Ichiwara",
"Hiroshi Ito",
"Kenjiro Yamamoto",
"Hiroki Mori",
"Tetsuya Ogata",
"Hideyuki Ichiwara",
"Hiroshi Ito",
"Kenjiro Yamamoto",
"Hiroki Mori",
"Tetsuya Ogata"
] |
We have developed an autonomous robot motion generation model based on distributed and integrated multimodal learning. Since each modality used as a robot's senses, such as image, joint angle, and torque, has a different physical meaning and time characteristic, the generation of autonomous motions using multimodal learning has sometimes failed due to overlearning in one of the modalities. Inspire...
|
Using Memory-Based Learning to Solve Tasks with State-Action Constraints
|
https://ieeexplore.ieee.org/document/10161154/
|
[
"Mrinal Verghese",
"Christopher Atkeson",
"Mrinal Verghese",
"Christopher Atkeson"
] |
Tasks where the set of possible actions depend discontinuously on the state pose a significant challenge for current reinforcement learning algorithms. For example, a locked door must be first unlocked, and then the handle turned before the door can be opened. The sequential nature of these tasks makes obtaining final rewards difficult, and transferring information between task variants using cont...
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Structured Motion Generation with Predictive Learning: Proposing Subgoal for Long-Horizon Manipulation
|
https://ieeexplore.ieee.org/document/10161046/
|
[
"Namiko Saito",
"João Moura",
"Tetsuya Ogata",
"Marina Y. Aoyama",
"Shingo Murata",
"Shigeki Sugano",
"Sethu Vijayakumar",
"Namiko Saito",
"João Moura",
"Tetsuya Ogata",
"Marina Y. Aoyama",
"Shingo Murata",
"Shigeki Sugano",
"Sethu Vijayakumar"
] |
For assisting humans in their daily lives, robots need to perform long-horizon tasks, such as tidying up a room or preparing a meal. One effective strategy for handling a long-horizon task is to break it down into short-horizon subgoals, that the robot can execute sequentially. In this paper, we propose extending a predictive learning model using deep neural networks (DNN) with a Subgoal Proposal ...
|
Sequence-Agnostic Multi-Object Navigation
|
https://ieeexplore.ieee.org/document/10160259/
|
[
"Nandiraju Gireesh",
"Ayush Agrawal",
"Ahana Datta",
"Snehasis Banerjee",
"Mohan Sridharan",
"Brojeshwar Bhowmick",
"Madhava Krishna",
"Nandiraju Gireesh",
"Ayush Agrawal",
"Ahana Datta",
"Snehasis Banerjee",
"Mohan Sridharan",
"Brojeshwar Bhowmick",
"Madhava Krishna"
] |
The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or a factory. Existing methods for MultiON have viewed this as a direct extension of Object Navigation (ON), the task of localising an instance of one object class, and are pre-sequenced, i.e., the sequence in which the obj...
|
Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies
|
https://ieeexplore.ieee.org/document/10160650/
|
[
"Chung Hee Kim",
"George Kantor",
"Chung Hee Kim",
"George Kantor"
] |
In this work, we present a method to extract the skeleton of a self-occluded tree canopy by estimating the unobserved structures of the tree. A tree skeleton compactly describes the topological structure and contains useful information such as branch geometry, positions and hierarchy. This can be critical to planning contact interactions for agricultural manipulation, yet is difficult to gain due ...
|
Statistical shape representations for temporal registration of plant components in 3D
|
https://ieeexplore.ieee.org/document/10160709/
|
[
"Karoline Heiwolt",
"Cengiz Öztireli",
"Grzegorz Cielniak",
"Karoline Heiwolt",
"Cengiz Öztireli",
"Grzegorz Cielniak"
] |
Plants are dynamic organisms and understanding temporal variations in vegetation is an essential problem for robots in the wild. However, associating repeated 3D scans of plants across time is challenging. A key step in this process is re-identifying and tracking the same individual plant components over time. Previously, this has been achieved by comparing their global spatial or topological loca...
|
3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection
|
https://ieeexplore.ieee.org/document/10161400/
|
[
"Harry Freeman",
"Eric Schneider",
"Chung Hee Kim",
"Moonyoung Lee",
"George Kantor",
"Harry Freeman",
"Eric Schneider",
"Chung Hee Kim",
"Moonyoung Lee",
"George Kantor"
] |
In this paper, we present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments. This is achieved with a novel reconstruction approach that uses seeds as semantic landmarks in both 2D and 3D. To evaluate the performance, we develop a new metric for assessing the quality of reconstructed point clouds without ground-truth. Finally, a counting method...
|
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
|
https://ieeexplore.ieee.org/document/10160918/
|
[
"Gianmarco Roggiolani",
"Matteo Sodano",
"Tiziano Guadagnino",
"Federico Magistri",
"Jens Behley",
"Cyrill Stachniss",
"Gianmarco Roggiolani",
"Matteo Sodano",
"Tiziano Guadagnino",
"Federico Magistri",
"Jens Behley",
"Cyrill Stachniss"
] |
Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accurately estimating plant traits such as the number of leaves, leaf area, and the plant size. In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB d...
|
Target-Aware Implicit Mapping for Agricultural Crop Inspection
|
https://ieeexplore.ieee.org/document/10160487/
|
[
"Shane Kelly",
"Alessandro Riccardi",
"Elias Marks",
"Federico Magistri",
"Tiziano Guadagnino",
"Margarita Chli",
"Cyrill Stachniss",
"Shane Kelly",
"Alessandro Riccardi",
"Elias Marks",
"Federico Magistri",
"Tiziano Guadagnino",
"Margarita Chli",
"Cyrill Stachniss"
] |
Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural enviro...
|
Robust Plant Localization and Phenotyping in Dense 3D Point Clouds for Precision Agriculture
|
https://ieeexplore.ieee.org/document/10161078/
|
[
"Henry J. Nelson",
"Christopher E. Smith",
"Athanasios Bacharis",
"Nikolaos P. Papanikolopoulos",
"Henry J. Nelson",
"Christopher E. Smith",
"Athanasios Bacharis",
"Nikolaos P. Papanikolopoulos"
] |
The determination of a crop's growth-stage is critical information for precision agriculture. Estimates of the growth-stage are used to guide irrigation and the application of agrochemicals. Of particular importance is the use of fertilizers, however, growth-stage estimates may also suggest further investigation of potential crop infections and infestations. Traditionally, the growth-stage is base...
|
Neural-Kalman GNSS/INS Navigation for Precision Agriculture
|
https://ieeexplore.ieee.org/document/10161351/
|
[
"Yayun Du",
"Swapnil Sayan Saha",
"Sandeep Singh Sandha",
"Arthur Lovekin",
"Jason Wu",
"S. Siddharth",
"Mahesh Chowdhary",
"Mohammad Khalid Jawed",
"Mani Srivastava",
"Yayun Du",
"Swapnil Sayan Saha",
"Sandeep Singh Sandha",
"Arthur Lovekin",
"Jason Wu",
"S. Siddharth",
"Mahesh Chowdhary",
"Mohammad Khalid Jawed",
"Mani Srivastava"
] |
Precision agricultural robots require high-resolution navigation solutions. In this paper, we introduce a robust neural-inertial sequence learning approach to track such robots with ultra-intermittent GNSS updates. First, we propose an ultra-lightweight neural-Kalman filter that can track agricultural robots within 1.4 m (1.4–5.8× better than competing techniques), while tracking within 2.75 m wit...
|
Fruit Tracking Over Time Using High-Precision Point Clouds
|
https://ieeexplore.ieee.org/document/10161350/
|
[
"Alessandro Riccardi",
"Shane Kelly",
"Elias Marks",
"Federico Magistri",
"Tiziano Guadagnino",
"Jens Behley",
"Maren Bennewitz",
"Cyrill Stachniss",
"Alessandro Riccardi",
"Shane Kelly",
"Elias Marks",
"Federico Magistri",
"Tiziano Guadagnino",
"Jens Behley",
"Maren Bennewitz",
"Cyrill Stachniss"
] |
Monitoring the traits of plants and fruits is a fundamental task in horticulture. With accurate measurements, farmers can predict the yield of their crops and use this information for making informed management decisions, and breeders can use it for variety selection. Agricultural robotic applications promise to automate this monitoring task. In this paper, we address the problem of monitoring fru...
|
A MySQL Database for the Systematic Configuration Selection of Redundant Manipulators when Path Planning in Confined Spaces
|
https://ieeexplore.ieee.org/document/10160417/
|
[
"Kat Styles Wood",
"Thomas B. Scott",
"Antonia Tzemanaki",
"Kat Styles Wood",
"Thomas B. Scott",
"Antonia Tzemanaki"
] |
Redundant manipulators offer a continuum of joint configurations which satisfy a specific end-effector pose, an advantage when operating within confined spaces. This, how-ever, challenges a controller to select a single goal configuration from a wide range when path planning. This paper outlines the use of the MySQL database management system for systematic goal selection during redundant manipula...
|
Reinforcement Learning Control of a Reconfigurable Planar Cable Driven Parallel Manipulator
|
https://ieeexplore.ieee.org/document/10160498/
|
[
"Adhiti Raman",
"Ameya Salvi",
"Matthias Schmid",
"Venkat Krovi",
"Adhiti Raman",
"Ameya Salvi",
"Matthias Schmid",
"Venkat Krovi"
] |
Cable driven parallel robots (CDPRs) are often challenging to model and to dynamically control due to the inherent flexibility and elasticity of the cables. The additional inclusion of online geometric reconfigurability to a CDPR results in a complex underdetermined system with highly non-linear dynamics. The necessary (numerical) redundancy resolution requires multiple layers of optimization rend...
|
Intuitive Telemanipulation of Hyper-Redundant Snake Robots within Locomotion and Reorientation using Task-Priority Inverse Kinematics
|
https://ieeexplore.ieee.org/document/10161124/
|
[
"Tim-Lukas Habich",
"Melvin Hueter",
"Moritz Schappler",
"Svenja Spindeldreier",
"Tim-Lukas Habich",
"Melvin Hueter",
"Moritz Schappler",
"Svenja Spindeldreier"
] |
Snake robots offer considerable potential for endoscopic interventions due to their ability to follow curvilinear paths. Telemanipulation is an open problem due to hyper-redundancy, as input devices only allow a specification of six degrees of freedom. Our work addresses this by presenting a unified telemanipulation strategy which enables follow-the-leader locomotion and reorientation keeping the ...
|
An equivalent two section method for calculating the workspace of multi-segment continuum robots
|
https://ieeexplore.ieee.org/document/10160611/
|
[
"Yeman Fan",
"Dikai Liu",
"Yeman Fan",
"Dikai Liu"
] |
Obtaining the shape and size of a robot's workspace is essential for both its design and control. However, determining the accurate workspace of a multi-segment continuum robot by graphic or analytical methods is a challenging task due to its inherent flexibility and complex structure. Existing numerical methods have limitations when applied to a continuum robot. This paper presents an Equivalent ...
|
On Locally Optimal Redundancy Resolution using the Basis of the Null Space
|
https://ieeexplore.ieee.org/document/10161181/
|
[
"Eugenio Monari",
"Yi Chen",
"Rocco Vertechy",
"Eugenio Monari",
"Yi Chen",
"Rocco Vertechy"
] |
This paper presents two methods for the computation of the null space velocity command in redundant robots. Both these methods resort to the solution of a constrained optimization problem. The first one is a formalization of the traditional Gradient Projection Method (GPM) which guarantees the respect of the joint bounds and a gradual activation/deactivation of the null space command. The second o...
|
Optimal Parameterized Joints Selection to Improve Motion Planning Performance of Redundant Manipulators
|
https://ieeexplore.ieee.org/document/10160901/
|
[
"Bin Xie",
"Qingfeng Wang",
"Di Wu",
"Bin Xie",
"Qingfeng Wang",
"Di Wu"
] |
The redundant manipulators' analytical solutions can be obtained by the parameterization method. Multiple parameterized joints and their corresponding parametric representations exist for a redundant manipulator. However, how to select the optimal parameterized joints has yet to be well-addressed. This paper delves into the mechanism of the parameterization method and proposes a method to select t...
|
A Kinematically Redundant (6+1)-dof Hybrid Parallel Robot for Delicate Physical Environment and Robot Interaction (pERI)
|
https://ieeexplore.ieee.org/document/10160676/
|
[
"Jehyeok Kim",
"Clément Gosselin",
"Jehyeok Kim",
"Clément Gosselin"
] |
A novel kinematically redundant 6+1-degree-of-freedom (dof) spatial hybrid parallel robot is proposed. Each of the two legs of the robot has a fully parallel structure to minimize the moving inertia by mounting actuators on the base. The kinematic model of each leg and overall robot architecture is developed based on the constraint conditions of the robot geometry. The singularity analysis of legs...
|
Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators
|
https://ieeexplore.ieee.org/document/10161426/
|
[
"Minsung Yoon",
"Mincheul Kang",
"Daehyung Park",
"Sung-Eui Yoon",
"Minsung Yoon",
"Mincheul Kang",
"Daehyung Park",
"Sung-Eui Yoon"
] |
Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-trivial and requires a considerable time budget due to the extremely large space of the solution traj...
|
Kinematic Analysis and Design of a Novel (6+3)-DoF Parallel Robot with Fixed Actuators
|
https://ieeexplore.ieee.org/document/10160533/
|
[
"Arda Yiğit",
"David Breton",
"Zhou Zhou",
"Thierry Laliberté",
"Clément Gosselin",
"Arda Yiğit",
"David Breton",
"Zhou Zhou",
"Thierry Laliberté",
"Clément Gosselin"
] |
A novel kinematically redundant ($6+3$) -DoF parallel robot is presented in this paper. Three identical 3-DoF RU/2-RUS legs are attached to a configurable platform through spherical joints. With the selected leg mechanism, the motors are mounted at the base, reducing the reflected inertia. The robot is intended to be actuated with direct-drive motors in order to perform intuitive physical human-ro...
|
RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks
|
https://ieeexplore.ieee.org/document/10161311/
|
[
"Yeping Wang",
"Pragathi Praveena",
"Daniel Rakita",
"Michael Gleicher",
"Yeping Wang",
"Pragathi Praveena",
"Daniel Rakita",
"Michael Gleicher"
] |
Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks ...
|
Contact Based Turning Gait of a Novel Legged-Wheeled Quadruped
|
https://ieeexplore.ieee.org/document/10161241/
|
[
"Alper Yeldan",
"Abhimanyu Arora",
"Gim Song Soh",
"Alper Yeldan",
"Abhimanyu Arora",
"Gim Song Soh"
] |
How does a wheeled robot move and turn? The answer is straightforward for a conventional wheeled robot, but it is not so easy for a robot with a discrete wheel design. Regular wheeled robots always have four contact points, resulting in static stability during locomotion. However, QuadRunner's novel leg mechanism provides only a semi-circular wheel shape, and proper gait planning is needed to go s...
|
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