Datasets:
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observation.image
image | observation.state
sequence | action
sequence | episode_index
int64 | frame_index
int64 | timestamp
float32 | next.reward
float32 | next.done
bool | index
int64 | task_index
int64 |
---|---|---|---|---|---|---|---|---|---|
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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] | 0 | 22 | 1.466667 | -0.818921 | false | 22 | 0 |
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[
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[
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] | [
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] | 0 | 24 | 1.6 | -0.690167 | true | 24 | 0 |
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[
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] | 1 | 0 | 0 | -0.869427 | false | 25 | 0 |
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[
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] | [
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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End of preview. Expand
in Dataset Viewer.
This dataset was created using LeRobot.
Dataset Structure
{
"codebase_version": "v2.0",
"robot_type": "unknown",
"total_episodes": 800,
"total_frames": 20000,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 15,
"splits": {
"train": "0:800"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {
"observation.image": {
"dtype": "image",
"shape": [
84,
84,
3
],
"names": [
"height",
"width",
"channel"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
4
],
"names": {
"motors": [
"motor_0",
"motor_1",
"motor_2",
"motor_3"
]
}
},
"action": {
"dtype": "float32",
"shape": [
3
],
"names": {
"motors": [
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}
},
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},
"task_index": {
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],
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}
}
}
Citation
BibTeX:
@inproceedings{Hansen2022tdmpc,
title={Temporal Difference Learning for Model Predictive Control},
author={Nicklas Hansen and Xiaolong Wang and Hao Su},
booktitle={ICML},
year={2022}
}
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