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Gymnasium/gymnasium/envs/mujoco/humanoid_v5.py
Mark Towers 433d7af1f9 Add python 3.13 and update pre-commit (#1390)
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__credits__ = ["Kallinteris-Andreas"]
import numpy as np
from gymnasium import utils
from gymnasium.envs.mujoco import MujocoEnv
from gymnasium.spaces import Box
DEFAULT_CAMERA_CONFIG = {
"trackbodyid": 1,
"distance": 4.0,
"lookat": np.array((0.0, 0.0, 2.0)),
"elevation": -20.0,
}
def mass_center(model, data):
mass = np.expand_dims(model.body_mass, axis=1)
xpos = data.xipos
return (np.sum(mass * xpos, axis=0) / np.sum(mass))[0:2].copy()
class HumanoidEnv(MujocoEnv, utils.EzPickle):
r"""
## Description
This environment is based on the environment introduced by Tassa, Erez and Todorov in ["Synthesis and stabilization of complex behaviors through online trajectory optimization"](https://ieeexplore.ieee.org/document/6386025).
The 3D bipedal robot is designed to simulate a human.
It has a torso (abdomen) with a pair of legs and arms, and a pair of tendons connecting the hips to the knees.
The legs each consist of three body parts (thigh, shin, foot), and the arms consist of two body parts (upper arm, forearm).
The goal of the environment is to walk forward as fast as possible without falling over.
## Action Space
```{figure} action_space_figures/humanoid.png
:name: humanoid
```
The action space is a `Box(-0.4, 0.4, (17,), float32)`. An action represents the torques applied at the hinge joints.
| Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Type (Unit) |
| --- | ---------------------------------------------------------------------------------- | ----------- | ----------- | -------------------------------- | ----- | ------------ |
| 0 | Torque applied on the hinge in the y-coordinate of the abdomen | -0.4 | 0.4 | abdomen_y | hinge | torque (N m) |
| 1 | Torque applied on the hinge in the z-coordinate of the abdomen | -0.4 | 0.4 | abdomen_z | hinge | torque (N m) |
| 2 | Torque applied on the hinge in the x-coordinate of the abdomen | -0.4 | 0.4 | abdomen_x | hinge | torque (N m) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
## Observation Space
The observation space consists of the following parts (in order)
- *qpos (22 elements by default):* The position values of the robot's body parts.
- *qvel (23 elements):* The velocities of these individual body parts (their derivatives).
- *cinert (130 elements):* Mass and inertia of the rigid body parts relative to the center of mass,
(this is an intermediate result of the transition).
It has shape 13*10 (*nbody * 10*).
(cinert - inertia matrix and body mass offset and body mass)
- *cvel (78 elements):* Center of mass based velocity.
It has shape 13 * 6 (*nbody * 6*).
(com velocity - velocity x, y, z and angular velocity x, y, z)
- *qfrc_actuator (17 elements):* Constraint force generated as the actuator force at each joint.
This has shape `(17,)` *(nv * 1)*.
- *cfrc_ext (78 elements):* This is the center of mass based external force on the body parts.
It has shape 13 * 6 (*nbody * 6*) and thus adds another 78 elements to the observation space.
(external forces - force x, y, z and torque x, y, z)
where *nbody* is the number of bodies in the robot,
and *nv* is the number of degrees of freedom (*= dim(qvel)*).
By default, the observation does not include the x- and y-coordinates of the torso.
These can be included by passing `exclude_current_positions_from_observation=False` during construction.
In this case, the observation space will be a `Box(-Inf, Inf, (350,), float64)`, where the first two observations are the x- and y-coordinates of the torso.
Regardless of whether `exclude_current_positions_from_observation` is set to `True` or `False`, the x- and y-coordinates are returned in `info` with the keys `"x_position"` and `"y_position"`, respectively.
By default, however, the observation space is a `Box(-Inf, Inf, (348,), float64)`, where the position and velocity elements are as follows:
| Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Type (Unit) |
| --- | --------------------------------------------------------------------------------------------------------------- | ---- | --- | -------------------------------- | ----- | -------------------------- |
| 0 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| 1 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 2 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 3 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 4 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) |
| 5 | z-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angle (rad) |
| 6 | y-angle of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angle (rad) |
| 7 | x-angle of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angle (rad) |
| 8 | x-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angle (rad) |
| 9 | z-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angle (rad) |
| 10 | y-coordinate of angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angle (rad) |
| 11 | angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angle (rad) |
| 12 | x-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angle (rad) |
| 13 | z-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angle (rad) |
| 14 | y-coordinate of angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angle (rad) |
| 15 | angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angle (rad) |
| 16 | coordinate-1 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder1 | hinge | angle (rad) |
| 17 | coordinate-2 (multi-axis) angle between torso and right arm (in right_upper_arm) | -Inf | Inf | right_shoulder2 | hinge | angle (rad) |
| 18 | angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angle (rad) |
| 19 | coordinate-1 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder1 | hinge | angle (rad) |
| 20 | coordinate-2 (multi-axis) angle between torso and left arm (in left_upper_arm) | -Inf | Inf | left_shoulder2 | hinge | angle (rad) |
| 21 | angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angle (rad) |
| 22 | x-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 23 | y-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 24 | z-coordinate velocity of the torso (centre) | -Inf | Inf | root | free | velocity (m/s) |
| 25 | x-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | angular velocity (rad/s) |
| 26 | y-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | angular velocity (rad/s) |
| 27 | z-coordinate angular velocity of the torso (centre) | -Inf | Inf | root | free | angular velocity (rad/s) |
| 28 | z-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_z | hinge | angular velocity (rad/s) |
| 29 | y-coordinate of angular velocity of the abdomen (in lower_waist) | -Inf | Inf | abdomen_y | hinge | angular velocity (rad/s) |
| 30 | x-coordinate of angular velocity of the abdomen (in pelvis) | -Inf | Inf | abdomen_x | hinge | angular velocity (rad/s) |
| 31 | x-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_x | hinge | angular velocity (rad/s) |
| 32 | z-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_z | hinge | angular velocity (rad/s) |
| 33 | y-coordinate of the angular velocity of the angle between pelvis and right hip (in right_thigh) | -Inf | Inf | right_hip_y | hinge | angular velocity (rad/s) |
| 34 | angular velocity of the angle between right hip and the right shin (in right_knee) | -Inf | Inf | right_knee | hinge | angular velocity (rad/s) |
| 35 | x-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_x | hinge | angular velocity (rad/s) |
| 36 | z-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_z | hinge | angular velocity (rad/s) |
| 37 | y-coordinate of the angular velocity of the angle between pelvis and left hip (in left_thigh) | -Inf | Inf | left_hip_y | hinge | angular velocity (rad/s) |
| 38 | angular velocity of the angle between left hip and the left shin (in left_knee) | -Inf | Inf | left_knee | hinge | angular velocity (rad/s) |
| 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 | angular velocity (rad/s) |
| 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 | angular velocity (rad/s) |
| 41 | angular velocity of the angle between right upper arm and right_lower_arm | -Inf | Inf | right_elbow | hinge | angular velocity (rad/s) |
| 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 | angular velocity (rad/s) |
| 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 | angular velocity (rad/s) |
| 44 | angular velocity of the angle between left upper arm and left_lower_arm | -Inf | Inf | left_elbow | hinge | angular velocity (rad/s) |
| excluded | x-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
| excluded | y-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) |
The body parts are:
| body part | id (for `v2`, `v3`, `v4)` | id (for `v5`) |
| ------------- | --- | --- |
| worldbody (note: all values are constant 0) | 0 |excluded|
| torso |1 | 0 |
| lwaist |2 | 1 |
| pelvis |3 | 2 |
| right_thigh |4 | 3 |
| right_sin |5 | 4 |
| right_foot |6 | 5 |
| left_thigh |7 | 6 |
| left_sin |8 | 7 |
| left_foot |9 | 8 |
| right_upper_arm |10 | 9 |
| right_lower_arm |11 | 10 |
| left_upper_arm |12 | 11 |
| left_lower_arm |13 | 12 |
The joints are:
| joint | id (for `v2`, `v3`, `v4)` | id (for `v5`) |
| ------------- | --- | --- |
| root (note: all values are constant 0) | 0 |excluded|
| root (note: all values are constant 0) | 1 |excluded|
| root (note: all values are constant 0) | 2 |excluded|
| root (note: all values are constant 0) | 3 |excluded|
| root (note: all values are constant 0) | 4 |excluded|
| root (note: all values are constant 0) | 5 |excluded|
| abdomen_z | 6 | 0 |
| abdomen_y | 7 | 1 |
| abdomen_x | 8 | 2 |
| right_hip_x | 9 | 3 |
| right_hip_z | 10 | 4 |
| right_hip_y | 11 | 5 |
| right_knee | 12 | 6 |
| left_hip_x | 13 | 7 |
| left_hiz_z | 14 | 8 |
| left_hip_y | 15 | 9 |
| left_knee | 16 | 10 |
| right_shoulder1 | 17 | 11 |
| right_shoulder2 | 18 | 12 |
| right_elbow | 19 | 13 |
| left_shoulder1 | 20 | 14 |
| left_shoulder2 | 21 | 15 |
| left_elfbow | 22 | 16 |
The (x,y,z) coordinates are translational DOFs, while the orientations are rotational DOFs expressed as quaternions.
One can read more about free joints in the [MuJoCo documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html).
**Note:**
When using Humanoid-v3 or earlier versions, problems have been reported when using a `mujoco-py` version > 2.0, resulting in contact forces always being 0.
Therefore, it is recommended to use a `mujoco-py` version < 2.0 when using the Humanoid environment if you want to report results with contact forces (if contact forces are not used in your experiments, you can use version > 2.0).
## Rewards
The total reward is: ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost - contact_cost*.
- *healthy_reward*:
Every timestep that the Humanoid is alive (see definition in section "Episode End"),
it gets a reward of fixed value `healthy_reward` (default is $5$).
- *forward_reward*:
A reward for moving forward,
this reward would be positive if the Humanoid moves forward (in the positive $x$ direction / in the right direction).
$w_{forward} \times \frac{dx}{dt}$, where
$dx$ is the displacement of the center of mass ($x_{after-action} - x_{before-action}$),
$dt$ is the time between actions, which depends on the `frame_skip` parameter (default is $5$),
and `frametime` which is $0.001$ - so the default is $dt = 5 \times 0.003 = 0.015$,
$w_{forward}$ is the `forward_reward_weight` (default is $1.25$).
- *ctrl_cost*:
A negative reward to penalize the Humanoid for taking actions that are too large.
$w_{control} \times \|action\|_2^2$,
where $w_{control}$ is `ctrl_cost_weight` (default is $0.1$).
- *contact_cost*:
A negative reward to penalize the Humanoid if the external contact forces are too large.
$w_{contact} \times clamp(contact\_cost\_range, \|F_{contact}\|_2^2)$, where
$w_{contact}$ is `contact_cost_weight` (default is $5\times10^{-7}$),
$F_{contact}$ are the external contact forces (see `cfrc_ext` section on observation).
`info` contains the individual reward terms.
**Note:** There is a bug in the `Humanoid-v4` environment that causes *contact_cost* to always be 0.
## Starting State
The initial position state is $[0.0, 0.0, 1.4, 1.0, 0.0, ... 0.0] + \mathcal{U}_{[-reset\_noise\_scale \times I_{24}, reset\_noise\_scale \times I_{24}]}$.
The initial velocity state is $\mathcal{U}_{[-reset\_noise\_scale \times I_{23}, reset\_noise\_scale \times I_{23}]}$.
where $\mathcal{U}$ is the multivariate uniform continuous distribution.
Note that the z- and x-coordinates are non-zero so that the humanoid can immediately stand up and face forward (x-axis).
## Episode End
### Termination
If `terminate_when_unhealthy is True` (the default), the environment terminates when the Humanoid is unhealthy.
The Humanoid is said to be unhealthy if any of the following happens:
1. The z-coordinate of the torso (the height) is **not** in the closed interval given by the `healthy_z_range` argument (default is $[1.0, 2.0]$).
### Truncation
The default duration of an episode is 1000 timesteps.
## Arguments
Humanoid provides a range of parameters to modify the observation space, reward function, initial state, and termination condition.
These parameters can be applied during `gymnasium.make` in the following way:
```python
import gymnasium as gym
env = gym.make('Humanoid-v5', ctrl_cost_weight=0.1, ....)
```
| Parameter | Type | Default | Description |
| -------------------------------------------- | --------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `xml_file` | **str** | `"humanoid.xml"` | Path to a MuJoCo model |
| `forward_reward_weight` | **float** | `1.25` | Weight for _forward_reward_ term (see `Rewards` section) |
| `ctrl_cost_weight` | **float** | `0.1` | Weight for _ctrl_cost_ term (see `Rewards` section) |
| `contact_cost_weight` | **float** | `5e-7` | Weight for _contact_cost_ term (see `Rewards` section) |
| `contact_cost_range` | **float** | `(-np.inf, 10.0)`| Clamps the _contact_cost_ term (see `Rewards` section) |
| `healthy_reward` | **float** | `5.0` | Weight for _healthy_reward_ term (see `Rewards` section) |
| `terminate_when_unhealthy` | **bool** | `True` | If `True`, issue a `terminated` signal is unhealthy (see `Episode End` section) |
| `healthy_z_range` | **tuple** | `(1.0, 2.0)` | The humanoid is considered healthy if the z-coordinate of the torso is in this range (see `Episode End` section) |
| `reset_noise_scale` | **float** | `1e-2` | Scale of random perturbations of initial position and velocity (see `Starting State` section) |
| `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 (see `Observation State` section) |
| `include_cinert_in_observation` | **bool** | `True` | Whether to include *cinert* elements in the observations (see `Observation State` section) |
| `include_cvel_in_observation` | **bool** | `True` | Whether to include *cvel* elements in the observations (see `Observation State` section) |
| `include_qfrc_actuator_in_observation` | **bool** | `True` | Whether to include *qfrc_actuator* elements in the observations (see `Observation State` section) |
| `include_cfrc_ext_in_observation` | **bool** | `True` | Whether to include *cfrc_ext* elements in the observations (see `Observation State` section) |
## Version History
* v5:
- Minimum `mujoco` version is now 2.3.3.
- Added support for fully custom/third party `mujoco` models using the `xml_file` argument (previously only a few changes could be made to the existing models).
- Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments.
- Added `env.observation_structure`, a dictionary for specifying the observation space compose (e.g. `qpos`, `qvel`), useful for building tooling and wrappers for the MuJoCo environments.
- Return a non-empty `info` with `reset()`, previously an empty dictionary was returned, the new keys are the same state information as `step()`.
- Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages.
- Fixed bug: `healthy_reward` was given on every step (even if the Humanoid was unhealthy), now it is only given when the Humanoid is healthy. The `info["reward_survive"]` is updated with this change (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/526)).
- Restored `contact_cost` and the corresponding `contact_cost_weight` and `contact_cost_range` arguments, with the same defaults as in `Humanoid-v3` (was removed in `v4`) (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/504)).
- Excluded the `cinert` & `cvel` & `cfrc_ext` of `worldbody` and `root`/`freejoint` `qfrc_actuator` from the observation space, as it was always 0 and thus provided no useful information to the agent, resulting in slightly faster training (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/204)).
- Restored the `xml_file` argument (was removed in `v4`).
- Added `include_cinert_in_observation`, `include_cvel_in_observation`, `include_qfrc_actuator_in_observation`, `include_cfrc_ext_in_observation` arguments to allow for the exclusion of observation elements from the observation space.
- Fixed `info["x_position"]` & `info["y_position"]` & `info["distance_from_origin"]` returning `xpos` instead of `qpos` based observations (`xpos` observations are behind 1 `mj_step()` more [here](https://github.com/deepmind/mujoco/issues/889#issuecomment-1568896388)) (related [GitHub issue #1](https://github.com/Farama-Foundation/Gymnasium/issues/521) & [GitHub issue #2](https://github.com/Farama-Foundation/Gymnasium/issues/539)).
- Added `info["tendon_length"]` and `info["tendon_velocity"]` containing observations of the Humanoid's 2 tendons connecting the hips to the knees.
- Renamed `info["reward_alive"]` to `info["reward_survive"]` to be consistent with the other environments.
- Renamed `info["reward_linvel"]` to `info["reward_forward"]` to be consistent with the other environments.
- Renamed `info["reward_quadctrl"]` to `info["reward_ctrl"]` to be consistent with the other environments.
- Removed `info["forward_reward"]` as it is equivalent to `info["reward_forward"]`.
* v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
* 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). Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
- Note: the environment robot model was slightly changed at `gym==0.21.0` and training results are not comparable with `gym<0.21` and `gym>=0.21` (related [GitHub PR](https://github.com/openai/gym/pull/932/files))
* v2: All continuous control environments now use mujoco-py >= 1.50. Moved to the [gymnasium-robotics repo](https://github.com/Farama-Foundation/gymnasium-robotics).
- Note: the environment robot model was slightly changed at `gym==0.21.0` and training results are not comparable with `gym<0.21` and `gym>=0.21` (related [GitHub PR](https://github.com/openai/gym/pull/932/files))
* v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
* v0: Initial versions release
"""
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
"rgbd_tuple",
],
}
def __init__(
self,
xml_file: str = "humanoid.xml",
frame_skip: int = 5,
default_camera_config: dict[str, float | int] = DEFAULT_CAMERA_CONFIG,
forward_reward_weight: float = 1.25,
ctrl_cost_weight: float = 0.1,
contact_cost_weight: float = 5e-7,
contact_cost_range: tuple[float, float] = (-np.inf, 10.0),
healthy_reward: float = 5.0,
terminate_when_unhealthy: bool = True,
healthy_z_range: tuple[float, float] = (1.0, 2.0),
reset_noise_scale: float = 1e-2,
exclude_current_positions_from_observation: bool = True,
include_cinert_in_observation: bool = True,
include_cvel_in_observation: bool = True,
include_qfrc_actuator_in_observation: bool = True,
include_cfrc_ext_in_observation: bool = True,
**kwargs,
):
utils.EzPickle.__init__(
self,
xml_file,
frame_skip,
default_camera_config,
forward_reward_weight,
ctrl_cost_weight,
contact_cost_weight,
contact_cost_range,
healthy_reward,
terminate_when_unhealthy,
healthy_z_range,
reset_noise_scale,
exclude_current_positions_from_observation,
include_cinert_in_observation,
include_cvel_in_observation,
include_qfrc_actuator_in_observation,
include_cfrc_ext_in_observation,
**kwargs,
)
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._contact_cost_weight = contact_cost_weight
self._contact_cost_range = contact_cost_range
self._healthy_reward = healthy_reward
self._terminate_when_unhealthy = terminate_when_unhealthy
self._healthy_z_range = healthy_z_range
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation
)
self._include_cinert_in_observation = include_cinert_in_observation
self._include_cvel_in_observation = include_cvel_in_observation
self._include_qfrc_actuator_in_observation = (
include_qfrc_actuator_in_observation
)
self._include_cfrc_ext_in_observation = include_cfrc_ext_in_observation
MujocoEnv.__init__(
self,
xml_file,
frame_skip,
observation_space=None,
default_camera_config=default_camera_config,
**kwargs,
)
self.metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
"rgbd_tuple",
],
"render_fps": int(np.round(1.0 / self.dt)),
}
obs_size = self.data.qpos.size + self.data.qvel.size
obs_size -= 2 * exclude_current_positions_from_observation
obs_size += self.data.cinert[1:].size * include_cinert_in_observation
obs_size += self.data.cvel[1:].size * include_cvel_in_observation
obs_size += (self.data.qvel.size - 6) * include_qfrc_actuator_in_observation
obs_size += self.data.cfrc_ext[1:].size * include_cfrc_ext_in_observation
self.observation_space = Box(
low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float64
)
self.observation_structure = {
"skipped_qpos": 2 * exclude_current_positions_from_observation,
"qpos": self.data.qpos.size
- 2 * exclude_current_positions_from_observation,
"qvel": self.data.qvel.size,
"cinert": self.data.cinert[1:].size * include_cinert_in_observation,
"cvel": self.data.cvel[1:].size * include_cvel_in_observation,
"qfrc_actuator": (self.data.qvel.size - 6)
* include_qfrc_actuator_in_observation,
"cfrc_ext": self.data.cfrc_ext[1:].size * include_cfrc_ext_in_observation,
"ten_length": 0,
"ten_velocity": 0,
}
@property
def healthy_reward(self):
return self.is_healthy * 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 contact_cost(self):
contact_forces = self.data.cfrc_ext
contact_cost = self._contact_cost_weight * np.sum(np.square(contact_forces))
min_cost, max_cost = self._contact_cost_range
contact_cost = np.clip(contact_cost, min_cost, max_cost)
return contact_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
def _get_obs(self):
position = self.data.qpos.flatten()
velocity = self.data.qvel.flatten()
if self._include_cinert_in_observation is True:
com_inertia = self.data.cinert[1:].flatten()
else:
com_inertia = np.array([])
if self._include_cvel_in_observation is True:
com_velocity = self.data.cvel[1:].flatten()
else:
com_velocity = np.array([])
if self._include_qfrc_actuator_in_observation is True:
actuator_forces = self.data.qfrc_actuator[6:].flatten()
else:
actuator_forces = np.array([])
if self._include_cfrc_ext_in_observation is True:
external_contact_forces = self.data.cfrc_ext[1:].flatten()
else:
external_contact_forces = np.array([])
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
observation = self._get_obs()
reward, reward_info = self._get_rew(x_velocity, action)
terminated = (not self.is_healthy) and self._terminate_when_unhealthy
info = {
"x_position": self.data.qpos[0],
"y_position": self.data.qpos[1],
"tendon_length": self.data.ten_length,
"tendon_velocity": self.data.ten_velocity,
"distance_from_origin": np.linalg.norm(self.data.qpos[0:2], ord=2),
"x_velocity": x_velocity,
"y_velocity": y_velocity,
**reward_info,
}
if self.render_mode == "human":
self.render()
# truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make`
return observation, reward, terminated, False, info
def _get_rew(self, x_velocity: float, action):
forward_reward = self._forward_reward_weight * x_velocity
healthy_reward = self.healthy_reward
rewards = forward_reward + healthy_reward
ctrl_cost = self.control_cost(action)
contact_cost = self.contact_cost
costs = ctrl_cost + contact_cost
reward = rewards - costs
reward_info = {
"reward_survive": healthy_reward,
"reward_forward": forward_reward,
"reward_ctrl": -ctrl_cost,
"reward_contact": -contact_cost,
}
return reward, reward_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
def _get_reset_info(self):
return {
"x_position": self.data.qpos[0],
"y_position": self.data.qpos[1],
"tendon_length": self.data.ten_length,
"tendon_velocity": self.data.ten_velocity,
"distance_from_origin": np.linalg.norm(self.data.qpos[0:2], ord=2),
}
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