423 lines
29 KiB
Python
423 lines
29 KiB
Python
import numpy as np
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from gymnasium import utils
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from gymnasium.envs.mujoco import MujocoEnv
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from gymnasium.spaces import Box
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DEFAULT_CAMERA_CONFIG = {
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"trackbodyid": 1,
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"distance": 4.0,
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"lookat": np.array((0.0, 0.0, 2.0)),
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"elevation": -20.0,
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}
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def mass_center(model, data):
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mass = np.expand_dims(model.body_mass, axis=1)
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xpos = data.xipos
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return (np.sum(mass * xpos, axis=0) / np.sum(mass))[0:2].copy()
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class HumanoidEnv(MujocoEnv, utils.EzPickle):
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"""
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## Description
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This environment is based on the environment introduced by Tassa, Erez and Todorov
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in ["Synthesis and stabilization of complex behaviors through online trajectory optimization"](https://ieeexplore.ieee.org/document/6386025).
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The 3D bipedal robot is designed to simulate a human. It has a torso (abdomen) with a pair of
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legs and arms. The legs each consist of three body parts, and the arms 2 body parts (representing the knees and
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elbows respectively). The goal of the environment is to walk forward as fast as possible without falling over.
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## Action Space
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The action space is a `Box(-1, 1, (17,), float32)`. An action represents the torques applied at the hinge joints.
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| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit |
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|-----|----------------------|---------------|----------------|---------------------------------------|-------|------|
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| 0 | Torque applied on the hinge in the y-coordinate of the abdomen | -0.4 | 0.4 | abdomen_y | hinge | torque (N m) |
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| 1 | Torque applied on the hinge in the z-coordinate of the abdomen | -0.4 | 0.4 | abdomen_z | hinge | torque (N m) |
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| 2 | Torque applied on the hinge in the x-coordinate of the abdomen | -0.4 | 0.4 | abdomen_x | hinge | torque (N m) |
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| 3 | Torque applied on the rotor between torso/abdomen and the right hip (x-coordinate) | -0.4 | 0.4 | right_hip_x (right_thigh) | hinge | torque (N m) |
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| 4 | Torque applied on the rotor between torso/abdomen and the right hip (z-coordinate) | -0.4 | 0.4 | right_hip_z (right_thigh) | hinge | torque (N m) |
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| 5 | Torque applied on the rotor between torso/abdomen and the right hip (y-coordinate) | -0.4 | 0.4 | right_hip_y (right_thigh) | hinge | torque (N m) |
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| 6 | Torque applied on the rotor between the right hip/thigh and the right shin | -0.4 | 0.4 | right_knee | hinge | torque (N m) |
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| 7 | Torque applied on the rotor between torso/abdomen and the left hip (x-coordinate) | -0.4 | 0.4 | left_hip_x (left_thigh) | hinge | torque (N m) |
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| 8 | Torque applied on the rotor between torso/abdomen and the left hip (z-coordinate) | -0.4 | 0.4 | left_hip_z (left_thigh) | hinge | torque (N m) |
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| 9 | Torque applied on the rotor between torso/abdomen and the left hip (y-coordinate) | -0.4 | 0.4 | left_hip_y (left_thigh) | hinge | torque (N m) |
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| 10 | Torque applied on the rotor between the left hip/thigh and the left shin | -0.4 | 0.4 | left_knee | hinge | torque (N m) |
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| 11 | Torque applied on the rotor between the torso and right upper arm (coordinate -1) | -0.4 | 0.4 | right_shoulder1 | hinge | torque (N m) |
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| 12 | Torque applied on the rotor between the torso and right upper arm (coordinate -2) | -0.4 | 0.4 | right_shoulder2 | hinge | torque (N m) |
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| 13 | Torque applied on the rotor between the right upper arm and right lower arm | -0.4 | 0.4 | right_elbow | hinge | torque (N m) |
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| 14 | Torque applied on the rotor between the torso and left upper arm (coordinate -1) | -0.4 | 0.4 | left_shoulder1 | hinge | torque (N m) |
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| 15 | Torque applied on the rotor between the torso and left upper arm (coordinate -2) | -0.4 | 0.4 | left_shoulder2 | hinge | torque (N m) |
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| 16 | Torque applied on the rotor between the left upper arm and left lower arm | -0.4 | 0.4 | left_elbow | hinge | torque (N m) |
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## Observation Space
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Observations consist of positional values of different body parts of the Humanoid,
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followed by the velocities of those individual parts (their derivatives) with all the
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positions ordered before all the velocities.
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By default, observations do not include the x- and y-coordinates of the torso. These may
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be included by passing `exclude_current_positions_from_observation=False` during construction.
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In that case, the observation space will be a `Box(-Inf, Inf, (378,), float64)` where the first two observations
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represent the x- and y-coordinates of the torso.
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Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x- and y-coordinates
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will be returned in `info` with keys `"x_position"` and `"y_position"`, respectively.
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However, by default, the observation is a `Box(-Inf, Inf, (376,), float64)`. The elements correspond to the following:
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| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit |
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| --- | --------------------------------------------------------------------------------------------------------------- | ---- | --- | -------------------------------- | ----- | -------------------------- |
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| 0 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
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| 1 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
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| 2 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
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| 3 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
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| 4 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
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| 5 | z-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angle (rad) |
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| 6 | y-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angle (rad) |
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| 7 | x-angle of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angle (rad) |
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| 8 | x-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angle (rad) |
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| 9 | z-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angle (rad) |
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| 10 | y-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angle (rad) |
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| 11 | angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angle (rad) |
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| 12 | x-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angle (rad) |
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| 13 | z-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angle (rad) |
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| 14 | y-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angle (rad) |
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| 15 | angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angle (rad) |
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| 16 | coordinate-1 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | angle (rad) |
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| 17 | coordinate-2 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | angle (rad) |
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| 18 | angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angle (rad) |
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| 19 | coordinate-1 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | angle (rad) |
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| 20 | coordinate-2 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | angle (rad) |
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| 21 | angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angle (rad) |
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| 22 | x-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
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| 23 | y-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
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| 24 | z-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
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| 25 | x-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
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| 26 | y-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
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| 27 | z-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | anglular velocity (rad/s) |
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| 28 | z-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | anglular velocity (rad/s) |
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| 29 | y-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | anglular velocity (rad/s) |
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| 30 | x-coordinate of angular velocity of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | aanglular velocity (rad/s) |
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| 31 | x-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | anglular velocity (rad/s) |
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| 32 | z-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | anglular velocity (rad/s) |
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| 33 | y-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | anglular velocity (rad/s) |
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| 34 | angular velocity of the angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | anglular velocity (rad/s) |
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| 35 | x-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | anglular velocity (rad/s) |
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| 36 | z-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | anglular velocity (rad/s) |
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| 37 | y-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | anglular velocity (rad/s) |
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| 38 | angular velocity of the angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | anglular velocity (rad/s) |
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| 39 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | anglular velocity (rad/s) |
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| 40 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | anglular velocity (rad/s) |
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| 41 | angular velocity of the angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | anglular velocity (rad/s) |
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| 42 | coordinate-1 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | anglular velocity (rad/s) |
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| 43 | coordinate-2 (multi-axis) of the angular velocity of the angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | anglular velocity (rad/s) |
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| 44 | angular velocity of the angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | anglular velocity (rad/s) |
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| excluded | x-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
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| excluded | y-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
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Additionally, after all the positional and velocity based values in the table,
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the observation contains (in order):
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- *cinert:* Mass and inertia of a single rigid body relative to the center of mass
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(this is an intermediate result of transition). It has shape 14*10 (*nbody * 10*)
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and hence adds to another 140 elements in the state space.
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- *cvel:* Center of mass based velocity. It has shape 14 * 6 (*nbody * 6*) and hence
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adds another 84 elements in the state space
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- *qfrc_actuator:* Constraint force generated as the actuator force. This has shape
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`(23,)` *(nv * 1)* and hence adds another 23 elements to the state space.
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- *cfrc_ext:* This is the center of mass based external force on the body. It has shape
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14 * 6 (*nbody * 6*) and hence adds to another 84 elements in the state space.
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where *nbody* stands for the number of bodies in the robot and *nv* stands for the
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number of degrees of freedom (*= dim(qvel)*)
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The body parts are:
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| id (for `v2`,`v3`,`v4`) | body part |
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| --- | ------------ |
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| 0 | worldBody (note: all values are constant 0) |
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| 1 | torso |
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| 2 | lwaist |
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| 3 | pelvis |
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| 4 | right_thigh |
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| 5 | right_sin |
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| 6 | right_foot |
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| 7 | left_thigh |
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| 8 | left_sin |
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| 9 | left_foot |
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| 10 | right_upper_arm |
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| 11 | right_lower_arm |
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| 12 | left_upper_arm |
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| 13 | left_lower_arm |
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The joints are:
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| id (for `v2`,`v3`,`v4`) | joint |
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| --- | ------------ |
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| 0 | root |
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| 1 | root |
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| 2 | root |
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| 3 | root |
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| 4 | root |
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| 5 | root |
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| 6 | abdomen_z |
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| 7 | abdomen_y |
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| 8 | abdomen_x |
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| 9 | right_hip_x |
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| 10 | right_hip_z |
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| 11 | right_hip_y |
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| 12 | right_knee |
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| 13 | left_hip_x |
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| 14 | left_hiz_z |
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| 15 | left_hip_y |
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| 16 | left_knee |
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| 17 | right_shoulder1 |
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| 18 | right_shoulder2 |
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| 19 | right_elbow|
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| 20 | left_shoulder1 |
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| 21 | left_shoulder2 |
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| 22 | left_elfbow |
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The (x,y,z) coordinates are translational DOFs while the orientations are rotational
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DOFs expressed as quaternions. One can read more about free joints on the
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[Mujoco Documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
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**Note:** Humanoid-v4 environment no longer has the following contact forces issue.
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If using previous Humanoid versions from v4, there have been reported issues that using a Mujoco-Py version > 2.0
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results in the contact forces always being 0. As such we recommend to use a Mujoco-Py
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version < 2.0 when using the Humanoid environment if you would like to report results
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with contact forces (if contact forces are not used in your experiments, you can use
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version > 2.0).
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## Rewards
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The reward consists of three parts:
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- *healthy_reward*: Every timestep that the humanoid is alive (see section Episode Termination for definition), it gets a reward of fixed value `healthy_reward`
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- *forward_reward*: A reward of walking forward which is measured as *`forward_reward_weight` *
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(average center of mass before action - average center of mass after action)/dt*.
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*dt* is the time between actions and is dependent on the frame_skip parameter
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(default is 5), where the frametime is 0.003 - making the default *dt = 5 * 0.003 = 0.015*.
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This reward would be positive if the humanoid walks forward (in positive x-direction). The calculation
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for the center of mass is defined in the `.py` file for the Humanoid.
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- *ctrl_cost*: A negative reward for penalising the humanoid if it has too
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large of a control force. If there are *nu* actuators/controls, then the control has
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shape `nu x 1`. It is measured as *`ctrl_cost_weight` * sum(control<sup>2</sup>)*.
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- *contact_cost*: A negative reward for penalising the humanoid if the external
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contact force is too large. It is calculated by clipping
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*`contact_cost_weight` * sum(external contact force<sup>2</sup>)* to the interval specified by `contact_cost_range`.
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The total reward returned is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost - contact_cost* and `info` will also contain the individual reward terms
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## Starting State
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All observations start in state
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(0.0, 0.0, 1.4, 1.0, 0.0 ... 0.0) with a uniform noise in the range
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of [-`reset_noise_scale`, `reset_noise_scale`] added to the positional and velocity values (values in the table)
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for stochasticity. Note that the initial z coordinate is intentionally
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selected to be high, thereby indicating a standing up humanoid. The initial
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orientation is designed to make it face forward as well.
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## Episode End
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The humanoid is said to be unhealthy if the z-position of the torso is no longer contained in the
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closed interval specified by the argument `healthy_z_range`.
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If `terminate_when_unhealthy=True` is passed during construction (which is the default),
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the episode ends when any of the following happens:
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1. Truncation: The episode duration reaches a 1000 timesteps
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3. Termination: The humanoid is unhealthy
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If `terminate_when_unhealthy=False` is passed, the episode is ended only when 1000 timesteps are exceeded.
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## Arguments
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No additional arguments are currently supported in v2 and lower.
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```python
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import gymnasium as gym
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env = gym.make('Humanoid-v4')
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```
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v3 and v4 take `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc.
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```python
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import gymnasium as gym
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env = gym.make('Humanoid-v4', ctrl_cost_weight=0.1, ....)
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```
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| Parameter | Type | Default | Description |
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| -------------------------------------------- | --------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `xml_file` | **str** | `"humanoid.xml"` | Path to a MuJoCo model |
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| `forward_reward_weight` | **float** | `1.25` | Weight for _forward_reward_ term (see section on reward) |
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| `ctrl_cost_weight` | **float** | `0.1` | Weight for _ctrl_cost_ term (see section on reward) |
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| `contact_cost_weight` | **float** | `5e-7` | Weight for _contact_cost_ term (see section on reward) |
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| `healthy_reward` | **float** | `5.0` | Constant reward given if the humanoid is "healthy" after timestep |
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| `terminate_when_unhealthy` | **bool** | `True` | If true, issue a done signal if the z-coordinate of the torso is no longer in the `healthy_z_range` |
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| `healthy_z_range` | **tuple** | `(1.0, 2.0)` | The humanoid is considered healthy if the z-coordinate of the torso is in this range |
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| `reset_noise_scale` | **float** | `1e-2` | Scale of random perturbations of initial position and velocity (see section on Starting State) |
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| `exclude_current_positions_from_observation` | **bool** | `True` | Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
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## Version History
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* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
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* v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
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* v2: All continuous control environments now use mujoco-py >= 1.50
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* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
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* v0: Initial versions release (1.0.0)
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"""
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metadata = {
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"render_modes": [
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"human",
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"rgb_array",
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"depth_array",
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],
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"render_fps": 67,
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}
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def __init__(
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self,
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forward_reward_weight=1.25,
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ctrl_cost_weight=0.1,
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healthy_reward=5.0,
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terminate_when_unhealthy=True,
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healthy_z_range=(1.0, 2.0),
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reset_noise_scale=1e-2,
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exclude_current_positions_from_observation=True,
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**kwargs,
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):
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utils.EzPickle.__init__(
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self,
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forward_reward_weight,
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ctrl_cost_weight,
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healthy_reward,
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terminate_when_unhealthy,
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healthy_z_range,
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reset_noise_scale,
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exclude_current_positions_from_observation,
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**kwargs,
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)
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self._forward_reward_weight = forward_reward_weight
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self._ctrl_cost_weight = ctrl_cost_weight
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self._healthy_reward = healthy_reward
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self._terminate_when_unhealthy = terminate_when_unhealthy
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self._healthy_z_range = healthy_z_range
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self._reset_noise_scale = reset_noise_scale
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self._exclude_current_positions_from_observation = (
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exclude_current_positions_from_observation
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)
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if exclude_current_positions_from_observation:
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(376,), dtype=np.float64
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)
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else:
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observation_space = Box(
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low=-np.inf, high=np.inf, shape=(378,), dtype=np.float64
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)
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MujocoEnv.__init__(
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self,
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"humanoid.xml",
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5,
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observation_space=observation_space,
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default_camera_config=DEFAULT_CAMERA_CONFIG,
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**kwargs,
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)
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@property
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def healthy_reward(self):
|
|
return (
|
|
float(self.is_healthy or self._terminate_when_unhealthy)
|
|
* self._healthy_reward
|
|
)
|
|
|
|
def control_cost(self, action):
|
|
control_cost = self._ctrl_cost_weight * np.sum(np.square(self.data.ctrl))
|
|
return control_cost
|
|
|
|
@property
|
|
def is_healthy(self):
|
|
min_z, max_z = self._healthy_z_range
|
|
is_healthy = min_z < self.data.qpos[2] < max_z
|
|
|
|
return is_healthy
|
|
|
|
@property
|
|
def terminated(self):
|
|
terminated = (not self.is_healthy) if self._terminate_when_unhealthy else False
|
|
return terminated
|
|
|
|
def _get_obs(self):
|
|
position = self.data.qpos.flat.copy()
|
|
velocity = self.data.qvel.flat.copy()
|
|
|
|
com_inertia = self.data.cinert.flat.copy()
|
|
com_velocity = self.data.cvel.flat.copy()
|
|
|
|
actuator_forces = self.data.qfrc_actuator.flat.copy()
|
|
external_contact_forces = self.data.cfrc_ext.flat.copy()
|
|
|
|
if self._exclude_current_positions_from_observation:
|
|
position = position[2:]
|
|
|
|
return np.concatenate(
|
|
(
|
|
position,
|
|
velocity,
|
|
com_inertia,
|
|
com_velocity,
|
|
actuator_forces,
|
|
external_contact_forces,
|
|
)
|
|
)
|
|
|
|
def step(self, action):
|
|
xy_position_before = mass_center(self.model, self.data)
|
|
self.do_simulation(action, self.frame_skip)
|
|
xy_position_after = mass_center(self.model, self.data)
|
|
|
|
xy_velocity = (xy_position_after - xy_position_before) / self.dt
|
|
x_velocity, y_velocity = xy_velocity
|
|
|
|
ctrl_cost = self.control_cost(action)
|
|
|
|
forward_reward = self._forward_reward_weight * x_velocity
|
|
healthy_reward = self.healthy_reward
|
|
|
|
rewards = forward_reward + healthy_reward
|
|
|
|
observation = self._get_obs()
|
|
reward = rewards - ctrl_cost
|
|
terminated = self.terminated
|
|
info = {
|
|
"reward_linvel": forward_reward,
|
|
"reward_quadctrl": -ctrl_cost,
|
|
"reward_alive": healthy_reward,
|
|
"x_position": xy_position_after[0],
|
|
"y_position": xy_position_after[1],
|
|
"distance_from_origin": np.linalg.norm(xy_position_after, ord=2),
|
|
"x_velocity": x_velocity,
|
|
"y_velocity": y_velocity,
|
|
"forward_reward": forward_reward,
|
|
}
|
|
|
|
if self.render_mode == "human":
|
|
self.render()
|
|
return observation, reward, terminated, False, info
|
|
|
|
def reset_model(self):
|
|
noise_low = -self._reset_noise_scale
|
|
noise_high = self._reset_noise_scale
|
|
|
|
qpos = self.init_qpos + self.np_random.uniform(
|
|
low=noise_low, high=noise_high, size=self.model.nq
|
|
)
|
|
qvel = self.init_qvel + self.np_random.uniform(
|
|
low=noise_low, high=noise_high, size=self.model.nv
|
|
)
|
|
self.set_state(qpos, qvel)
|
|
|
|
observation = self._get_obs()
|
|
return observation
|