improved project structure and exposed methods

alr_envs/dmc/manipulation/reach_site/__init__.py

@@ -0,0 +1 @@
+from .mp_wrapper import MPWrapper

alr_envs/dmc/manipulation/reach_site/mp_wrapper.py

@@ -0,0 +1,38 @@
+from typing import Tuple, Union
+
+import numpy as np
+
+from mp_env_api import MPEnvWrapper
+
+
+class MPWrapper(MPEnvWrapper):
+
+    @property
+    def active_obs(self):
+        # Joint and target positions are randomized, velocities are always set to 0.
+        return np.hstack([
+            [True] * 3,  # target position
+            [True] * 12,  # sin/cos arm joint position
+            [True] * 6,  # arm joint torques
+            [False] * 6,  # arm joint velocities
+            [True] * 3,  # sin/cos hand joint position
+            [False] * 3,  # hand joint velocities
+            [True] * 3,  # hand pinch site position
+            [True] * 9,  # pinch site rmat
+        ])
+
+    @property
+    def current_pos(self) -> Union[float, int, np.ndarray]:
+        return self.env.physics.named.data.qpos[:]
+
+    @property
+    def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
+        return self.env.physics.named.data.qvel[:]
+
+    @property
+    def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
+        raise ValueError("Goal position is not available and has to be learnt based on the environment.")
+
+    @property
+    def dt(self) -> Union[float, int]:
+        return self.env.dt