Skip to content

Commit

Permalink
Merge branch 'mc24_p2' of https://github.com/MyoHub/myosuite into mc2…
Browse files Browse the repository at this point in the history 
…4_p2
  • Loading branch information
@cherylwang20
cherylwang20 committed Sep 18, 2024
2 parents 233c386 + e26326a commit 0407041
Show file tree
Hide file tree
Showing 2 changed files with 77 additions and 25 deletions.
97 changes: 73 additions & 24 deletions docs/source/challenge-doc.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@ MyoChallenge-2024 Documentations
* :ref:`challenge24_manipulation`
* :ref:`challenge24_locomotion`
* :ref:`challenge24_tutorial`
* :ref:`challenge24_disclaimer`



Expand Down Expand Up @@ -32,7 +33,7 @@ Objective


Move the object between two locations with a handover between a hand and a prosthesis. The task parameters will be randomized to provide a comprehensive
test to the controller model performance. The randomization will include but not limited to: object type, object weight and even friction during each environmental reset.
test to the controller's performance. The randomization will include but not limited to: object type, object weight and even friction during each environmental reset.



Expand All @@ -54,11 +55,51 @@ Observation Space

**Observations Space**


The obs_dict variable contains useful observations for completing the task. Please note that participants are encourage to
modify the obs_dict to customize their reward computations; yet values directly obtained outside the obs_dict, or directly from
the simulator might not be accessible in submissions.

The obs_dict variable contains useful features that are used to create observation vectors (configured via obs_keys) and for computing environment rewards (configures get_reward_dict(.)
in via weighted_reward_keys).

During training, participants are encouraged to add new keys to the obs_dict to further aid their reward computations. Note that the values obtained outside the provided obs_dict,
or directly from the simulator might not be accessible during submission evaluations.


.. temporary change backup
.. +-----------------------------------------+-----------------------------+-----------------+
.. | **Description** | **Access** | **Dimension** |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Time | obs_dict['time'] | (1x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Joint positions of myoArm | obs_dict['myohand_qpos'] | (38x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Joint velocity of myoArm | obs_dict['myohand_qvel'] | (38x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Joint positions of MPL | obs_dict['pros_hand_qpos'] | (27x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Joint velocity of MPL | obs_dict['pros_hand_qvel'] | (26x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Joint positions of object | obs_dict['object_qpos'] | (7x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Joint velocity of object | obs_dict['object_qvel'] | (6x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Contact information of object | obs_dict['touching_body'] | (5x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Starting position | obs_dict['start_pos'] | (2x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Goal position | obs_dict['goal_pos'] | (2x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Muscle activation of myoHand | obs_dict['act'] | (63x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Palm location | obs_dict['palm_pos'] | (3x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Finger tip location | obs_dict['fin_i'] | (3x5) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | MPL palm location | obs_dict['Rpalm_pos'] | (3x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Object position | obs_dict['obj_pos'] | (3x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Hand reaching error | obs_dict['reach_err'] | (3x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
.. | Hand passing error | obs_dict['pass_err'] | (3x1) |
.. +-----------------------------------------+-----------------------------+-----------------+
+-----------------------------------------+-----------------------------+-----------------+
Expand All @@ -78,22 +119,14 @@ the simulator might not be accessible in submissions.
+-----------------------------------------+-----------------------------+-----------------+
| Joint velocity of object | obs_dict['object_qvel'] | (6x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Touching information of object | obs_dict['touching_body'] | (5x1) |
| Contact information of object | obs_dict['touching_body'] | (5x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Starting position | obs_dict['start_pos'] | (2x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Goal position | obs_dict['goal_pos'] | (2x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Muscle activation of myoHand | obs_dict['act'] | (63x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Palm location | obs_dict['palm_pos'] | (3x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Finger tip location | obs_dict['fin_i'] | (3x5) |
+-----------------------------------------+-----------------------------+-----------------+
| MPL palm location | obs_dict['Rpalm_pos'] | (3x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Object position | obs_dict['obj_pos'] | (3x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Hand reaching error | obs_dict['reach_err'] | (3x1) |
+-----------------------------------------+-----------------------------+-----------------+
| Hand passing error | obs_dict['pass_err'] | (3x1) |
Expand All @@ -119,29 +152,33 @@ the simulator might not be accessible in submissions.

- Hand passing error measures the distance between the MPL and the object

- The manipulated object has full 6 degrees of freedom, its state described as a 7 dimensional value in position + quaternion format. Details can be found in "`freejoint <https://mujoco.readthedocs.io/en/stable/XMLreference.html#body-freejoint>`__"
- The manipulated object has full 6 degrees of freedom, its state described as a 7 dimensional value in position + quaternion format. Details can be found in "`mujoco-freejoint <https://mujoco.readthedocs.io/en/stable/XMLreference.html#body-freejoint>`__" page




**Variation on Object Properties**
The geometry, mass, and friction of the object will reset at the start of each episode.
Both the geometry and physical properties of the object as well as the environment can be sampled at the start of each episode to provide variability in the task. Provided
below is an example of how real-world scenarios is captured in the test environments we provide.

- Object scale: a +- change between 0% - 5%, 0% - 10% scale variations in respective geom directions
- Object Mass: an upper/lower bound of +-50 gms
- Object scale: a +- change in respective geom directions ( between 0% - 5%, 0% - 10% in TEST environment)
- Object Mass: an upper/lower bound of X gms (X = 50 in TEST environment)
- Object Friction: a +- change between 0 - 0.1, 0 - 0.001, 0 - 0.00002 from nominal value: [1.0, 0.005, 0.0001] in respective geom direction

Note that these distributions may be different in the final evaluation environment. Try to maintain the performance of your policies in as wide a range as possible.


**Success Condition**

- The object moved from start position to goal position. Both the MPL hand, and MyoHand, is required to touch the object for 100 timesteps
- Exerting a maximum contact force on the object, less than 1500N (subject to change based on submission)
- Exerting a maximum contact force on the object, less than 1500N (subject to change in final EVALUATION environment)
- Placing the object within 0.05 meters of the goal site on the pillar

**Ranking Criteria**
1. Task success rate (success_attempt / total_attempt)
2. Time to complete the task (success_attempt + failed_attempt)
3. Muscle activation
4. Distance from goal site (only if tie in previous metrics)
1. Task success rate (successful_attempts / total_attempts)
2. Total time to complete the task (failed_attemps will be punished for a time of full episode length)
3. Minimum total muscle activation
4. Minimum total distance from goal position (only if tie in previous metrics)



Expand Down Expand Up @@ -384,3 +421,15 @@ Links are available for `manipulation <https://colab.research.google.com/drive/1
# Reset training if env is terminated
if terminated:
next_obs, info = env.reset()
.. _challenge24_disclaimer:

Challenge disclaimer on test and evaluation environments
--------------------------------------------------------------

This challenge aims to provide a simulated environment that captures the complexity of real-world scenarios. In order for participants to familiarise themselves with the tasks,
we have opened the portal for a TEST environment to begin with. Please note that even though the tasks and evaluation criteria will stay the same, there might be difference in the
changing factors' distributions in the final EVALUATION environment. Please try to maintain the robustness of your policies in as wide a range as possible.
5 changes: 4 additions & 1 deletion myosuite/envs/myo/myochallenge/bimanual_v0.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -257,6 +257,9 @@ def get_reward_dict(self, obs_dict):
elbow_err = 5 * np.exp(-10 * (obs_dict['elbow_fle'][0] - 1.) ** 2) - 5
goal_dis = np.array(
[[np.abs(np.linalg.norm(obj_pos[:2] - goal_pos, axis=-1))]])

isTimeLimit = obs_dict['time'] > 3.0

rwd_dict = collections.OrderedDict(
(
# Optional Keys
Expand Down Expand Up @@ -323,7 +326,7 @@ def get_metrics(self, paths, successful_steps=5):
goal_dist = np.mean([np.mean(p['env_infos']['rwd_dict']['goal_dist']) for p in paths])

# average activations over entire trajectory (can be shorter than horizon, if done) realized
effort = -1.0 * np.mean([np.mean(p['env_infos']['rwd_dict']['act']) for p in paths])
effort = 1.0 * np.mean([np.mean(p['env_infos']['rwd_dict']['act']) for p in paths])

metrics = {
'score': score,
Expand Down

0 comments on commit 0407041

Please sign in to comment.