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MyoChallenge Manipulation Track Metric #213

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merged 5 commits into from
Aug 27, 2024
Merged

MyoChallenge Manipulation Track Metric #213

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b1ec80a
adding evaluation metric for manipulation
cherylwang20 Aug 26, 2024
877f306
Merge branch 'MyoHub:main' into myochal-mani-p2
cherylwang20 Aug 26, 2024
ad5aebc
hotfix for linux compilation error
cherylwang20 Aug 26, 2024
710b0fe
Merge branch 'myochal-mani-p2' of https://github.com/Balint-H/myosuit…
cherylwang20 Aug 26, 2024
3281da5
Update bimanual_v0.py
cherylwang20 Aug 26, 2024
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22 changes: 11 additions & 11 deletions myosuite/envs/myo/myochallenge/bimanual_v0.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,7 +111,7 @@ def _setup(self,

# check whether the object experience force over max force
self.over_max = False
self.max_force = max_force
self.max_force = 0

self.touch_history = []

Expand Down Expand Up @@ -177,7 +177,8 @@ def get_obs_dict(self, sim):

current_force = sim.data.sensordata[0]
if current_force > self.max_force:
self.over_max = True
self.max_force = current_force
obs_dict['max_force'] = np.array([self.max_force])

obs_vec = self._obj_label_to_obs(touching_objects)
obs_dict["touching_body"] = obs_vec
Expand Down Expand Up @@ -236,7 +237,8 @@ def get_reward_dict(self, obs_dict):
("pass_err", pass_dist + np.log(pass_dist + 1e-3)),
# Must keys
("sparse", 0),
("solved", self.check_solve(goal_dis)),
("goal_dist", goal_dis),
("solved", goal_dis < 0.05),
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can we have this as a variable?

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Updated in this commit: 3281da5

("done", False),
)
)
Expand All @@ -258,12 +260,6 @@ def step(self, a, **kwargs):
- self.sim.model.actuator_ctrlrange[robotic_act_ind, 0]) / 2.0)
return super().step(processed_controls, **kwargs)

def check_solve(self, goal_dis):
if goal_dis > 0.01:
return False
if self.over_max == True:
return False
return True

def get_metrics(self, paths, successful_steps=5):
"""
Expand All @@ -280,15 +276,19 @@ def get_metrics(self, paths, successful_steps=5):
num_success += 1
score = num_success / num_paths

times = np.mean([np.round(p['env_infos']['obs_dict']['time'][-1], 2) for p in paths])
times = np.mean([np.round(p['env_infos']['obs_dict']['time'][-1], 5) for p in paths])
max_force = np.mean([np.round(p['env_infos']['obs_dict']['max_force'][-1], 5) for p in paths])
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_reg']) for p in paths])
effort = -1.0 * np.mean([np.mean(p['env_infos']['rwd_dict']['act']) for p in paths])

metrics = {
'score': score,
'time': times,
'effort': effort,
'peak force': max_force,
'goal dist': goal_dist,
}
return metrics

Expand Down
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