Package xarm

envs/sim_xarm/xarm/tasks/reach.py

@@ -0,0 +1,44 @@
+import numpy as np
+
+from xarm import Base
+
+
+class Reach(Base):
+    def __init__(self):
+        super().__init__("reach")
+
+    def _reset_sim(self):
+        self._act_magnitude = 0
+        super()._reset_sim()
+
+    def is_success(self):
+        return np.linalg.norm(self.eef - self.goal) <= 0.05
+
+    def get_reward(self):
+        dist = np.linalg.norm(self.eef - self.goal)
+        penalty = self._act_magnitude**2
+        return -(dist + 0.15 * penalty)
+
+    def _get_obs(self):
+        eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
+        gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
+        eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
+        obs = np.concatenate(
+            [eef, eef_velp, goal, eef - goal, np.array([np.linalg.norm(eef - goal), gripper_angle])], axis=0
+        )
+        return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
+
+    def _sample_goal(self):
+        # Gripper
+        gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
+        super()._set_gripper(gripper_pos, self.gripper_rotation)
+
+        # Goal
+        self.goal = np.array([1.550, 0.287, 0.580])
+        self.goal[:2] += self.np_random.uniform(-0.125, 0.125, size=2)
+        self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
+        return self.goal
+
+    def step(self, action):
+        self._act_magnitude = np.linalg.norm(action[:3])
+        return super().step(action)