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05b8dea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | import os
if os.environ.get("ISR_MEMDECODE") == "1":
try: import memdecode # noqa
except Exception as e: print("memdecode patch failed:", e)
if os.environ.get("ISR_GYM_ALOHA") == "1":
try:
import gym_aloha # noqa (registers gym_aloha/* envs in every process incl. forkserver children)
from gym_aloha.env import AlohaEnv
if not hasattr(AlohaEnv, "task_description"): AlohaEnv.task_description = property(lambda self: f"aloha {getattr(self, 'task', '')}") # lerobot_eval requires it
except Exception as e: print("gym_aloha import failed:", e)
if os.environ.get("ISR_ALOHA_DEG") == "1": # policy trained in degrees; env speaks radians
try:
import numpy as np
from gym_aloha.env import AlohaEnv
_step, _reset = AlohaEnv.step, AlohaEnv.reset
def _o(o):
if isinstance(o, dict) and "agent_pos" in o: o["agent_pos"] = np.rad2deg(o["agent_pos"]).astype(np.float32)
return o
def step(self, action): o, r, te, tr, i = _step(self, np.deg2rad(np.asarray(action, dtype=np.float64))); return _o(o), r, te, tr, i
def reset(self, *a, **k): o, i = _reset(self, *a, **k); return _o(o), i
AlohaEnv.step, AlohaEnv.reset = step, reset
except Exception as e: print("aloha deg shim failed:", e)
if os.environ.get("ISR_ACTION_REPEAT", "1") != "1": # rate-matched deployment: hold each policy action N sim steps
try:
from gym_aloha.env import AlohaEnv
_N = int(os.environ["ISR_ACTION_REPEAT"]); _step_r = AlohaEnv.step
def step_rep(self, action):
tot = 0.0
for _ in range(_N):
o, r, te, tr, i = _step_r(self, action); tot += float(r)
if te or tr: break
return o, tot, te, tr, i
AlohaEnv.step = step_rep
except Exception as e: print("action repeat shim failed:", e)
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