import numpy as np from gym import utils from gym.envs.mujoco import MujocoEnv from gym.spaces import Box class PusherEnv(MujocoEnv, utils.EzPickle): """ ### Description "Pusher" is a multi-jointed robot arm which is very similar to that of a human. The goal is to move a target cylinder (called *object*) to a goal position using the robot's end effector (called *fingertip*). The robot consists of shoulder, elbow, forearm, and wrist joints. ### Action Space The action space is a `Box(-2, 2, (7,), float32)`. An action `(a, b)` represents the torques applied at the hinge joints. | Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit | |-----|--------------------------------------------------------------------|-------------|-------------|----------------------------------|-------|--------------| | 0 | Rotation of the panning the shoulder | -2 | 2 | r_shoulder_pan_joint | hinge | torque (N m) | | 1 | Rotation of the shoulder lifting joint | -2 | 2 | r_shoulder_lift_joint | hinge | torque (N m) | | 2 | Rotation of the shoulder rolling joint | -2 | 2 | r_upper_arm_roll_joint | hinge | torque (N m) | | 3 | Rotation of hinge joint that flexed the elbow | -2 | 2 | r_elbow_flex_joint | hinge | torque (N m) | | 4 | Rotation of hinge that rolls the forearm | -2 | 2 | r_forearm_roll_joint | hinge | torque (N m) | | 5 | Rotation of flexing the wrist | -2 | 2 | r_wrist_flex_joint | hinge | torque (N m) | | 6 | Rotation of rolling the wrist | -2 | 2 | r_wrist_roll_joint | hinge | torque (N m) | ### Observation Space Observations consist of - Angle of rotational joints on the pusher - Angular velocities of rotational joints on the pusher - The coordinates of the fingertip of the pusher - The coordinates of the object to be moved - The coordinates of the goal position The observation is a `ndarray` with shape `(23,)` where the elements correspond to the table below. An analogy can be drawn to a human arm in order to help understand the state space, with the words flex and roll meaning the same as human joints. | Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit | | --- | -------------------------------------------------------- | ---- | --- | -------------------------------- | -------- | ------------------------ | | 0 | Rotation of the panning the shoulder | -Inf | Inf | r_shoulder_pan_joint | hinge | angle (rad) | | 1 | Rotation of the shoulder lifting joint | -Inf | Inf | r_shoulder_lift_joint | hinge | angle (rad) | | 2 | Rotation of the shoulder rolling joint | -Inf | Inf | r_upper_arm_roll_joint | hinge | angle (rad) | | 3 | Rotation of hinge joint that flexed the elbow | -Inf | Inf | r_elbow_flex_joint | hinge | angle (rad) | | 4 | Rotation of hinge that rolls the forearm | -Inf | Inf | r_forearm_roll_joint | hinge | angle (rad) | | 5 | Rotation of flexing the wrist | -Inf | Inf | r_wrist_flex_joint | hinge | angle (rad) | | 6 | Rotation of rolling the wrist | -Inf | Inf | r_wrist_roll_joint | hinge | angle (rad) | | 7 | Rotational velocity of the panning the shoulder | -Inf | Inf | r_shoulder_pan_joint | hinge | angular velocity (rad/s) | | 8 | Rotational velocity of the shoulder lifting joint | -Inf | Inf | r_shoulder_lift_joint | hinge | angular velocity (rad/s) | | 9 | Rotational velocity of the shoulder rolling joint | -Inf | Inf | r_upper_arm_roll_joint | hinge | angular velocity (rad/s) | | 10 | Rotational velocity of hinge joint that flexed the elbow | -Inf | Inf | r_elbow_flex_joint | hinge | angular velocity (rad/s) | | 11 | Rotational velocity of hinge that rolls the forearm | -Inf | Inf | r_forearm_roll_joint | hinge | angular velocity (rad/s) | | 12 | Rotational velocity of flexing the wrist | -Inf | Inf | r_wrist_flex_joint | hinge | angular velocity (rad/s) | | 13 | Rotational velocity of rolling the wrist | -Inf | Inf | r_wrist_roll_joint | hinge | angular velocity (rad/s) | | 14 | x-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) | | 15 | y-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) | | 16 | z-coordinate of the fingertip of the pusher | -Inf | Inf | tips_arm | slide | position (m) | | 17 | x-coordinate of the object to be moved | -Inf | Inf | object (obj_slidex) | slide | position (m) | | 18 | y-coordinate of the object to be moved | -Inf | Inf | object (obj_slidey) | slide | position (m) | | 19 | z-coordinate of the object to be moved | -Inf | Inf | object | cylinder | position (m) | | 20 | x-coordinate of the goal position of the object | -Inf | Inf | goal (goal_slidex) | slide | position (m) | | 21 | y-coordinate of the goal position of the object | -Inf | Inf | goal (goal_slidey) | slide | position (m) | | 22 | z-coordinate of the goal position of the object | -Inf | Inf | goal | sphere | position (m) | ### Rewards The reward consists of two parts: - *reward_near *: This reward is a measure of how far the *fingertip* of the pusher (the unattached end) is from the object, with a more negative value assigned for when the pusher's *fingertip* is further away from the target. It is calculated as the negative vector norm of (position of the fingertip - position of target), or *-norm("fingertip" - "target")*. - *reward_dist *: This reward is a measure of how far the object is from the target goal position, with a more negative value assigned for object is further away from the target. It is calculated as the negative vector norm of (position of the object - position of goal), or *-norm("object" - "target")*. - *reward_control*: A negative reward for penalising the pusher if it takes actions that are too large. It is measured as the negative squared Euclidean norm of the action, i.e. as *- sum(action2)*. The total reward returned is ***reward*** *=* *reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near* Unlike other environments, Pusher does not allow you to specify weights for the individual reward terms. However, `info` does contain the keys *reward_dist* and *reward_ctrl*. Thus, if you'd like to weight the terms, you should create a wrapper that computes the weighted reward from `info`. ### Starting State All pusher (not including object and goal) states start in (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0). A uniform noise in the range [-0.005, 0.005] is added to the velocity attributes only. The velocities of the object and goal are permanently set to 0. The object's x-position is selected uniformly between [-0.3, 0] while the y-position is selected uniformly between [-0.2, 0.2], and this process is repeated until the vector norm between the object's (x,y) position and origin is not greater than 0.17. The goal always have the same position of (0.45, -0.05, -0.323). The default framerate is 5 with each frame lasting for 0.01, giving rise to a *dt = 5 * 0.01 = 0.05* ### Episode End The episode ends when any of the following happens: 1. Truncation: The episode duration reaches a 100 timesteps. 2. Termination: Any of the state space values is no longer finite. ### Arguments No additional arguments are currently supported (in v2 and lower), but modifications can be made to the XML file in the assets folder (or by changing the path to a modified XML file in another folder).. ``` env = gym.make('Pusher-v4') ``` There is no v3 for Pusher, unlike the robot environments where a v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. ### Version History * v4: all mujoco environments now use the mujoco bindings in mujoco>=2.1.3 * v2: All continuous control environments now use mujoco_py >= 1.50 * v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments. * v0: Initial versions release (1.0.0) """ metadata = { "render_modes": [ "human", "rgb_array", "depth_array", ], "render_fps": 20, } def __init__(self, **kwargs): utils.EzPickle.__init__(self, **kwargs) observation_space = Box(low=-np.inf, high=np.inf, shape=(23,), dtype=np.float64) MujocoEnv.__init__( self, "pusher.xml", 5, observation_space=observation_space, **kwargs ) def step(self, a): vec_1 = self.get_body_com("object") - self.get_body_com("tips_arm") vec_2 = self.get_body_com("object") - self.get_body_com("goal") reward_near = -np.linalg.norm(vec_1) reward_dist = -np.linalg.norm(vec_2) reward_ctrl = -np.square(a).sum() reward = reward_dist + 0.1 * reward_ctrl + 0.5 * reward_near self.do_simulation(a, self.frame_skip) if self.render_mode == "human": self.render() ob = self._get_obs() return ( ob, reward, False, False, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl), ) def viewer_setup(self): assert self.viewer is not None self.viewer.cam.trackbodyid = -1 self.viewer.cam.distance = 4.0 def reset_model(self): qpos = self.init_qpos self.goal_pos = np.asarray([0, 0]) while True: self.cylinder_pos = np.concatenate( [ self.np_random.uniform(low=-0.3, high=0, size=1), self.np_random.uniform(low=-0.2, high=0.2, size=1), ] ) if np.linalg.norm(self.cylinder_pos - self.goal_pos) > 0.17: break qpos[-4:-2] = self.cylinder_pos qpos[-2:] = self.goal_pos qvel = self.init_qvel + self.np_random.uniform( low=-0.005, high=0.005, size=self.model.nv ) qvel[-4:] = 0 self.set_state(qpos, qvel) return self._get_obs() def _get_obs(self): return np.concatenate( [ self.data.qpos.flat[:7], self.data.qvel.flat[:7], self.get_body_com("tips_arm"), self.get_body_com("object"), self.get_body_com("goal"), ] )