Package xarm

envs/sim_xarm/xarm/tasks/lift.py

@@ -0,0 +1,100 @@
+import numpy as np
+
+from xarm import Base
+
+
+class Lift(Base):
+    def __init__(self):
+        self._z_threshold = 0.15
+        super().__init__("lift")
+
+    @property
+    def z_target(self):
+        return self._init_z + self._z_threshold
+
+    def is_success(self):
+        return self.obj[2] >= self.z_target
+
+    def get_reward(self):
+        reach_dist = np.linalg.norm(self.obj - self.eef)
+        reach_dist_xy = np.linalg.norm(self.obj[:-1] - self.eef[:-1])
+        pick_completed = self.obj[2] >= (self.z_target - 0.01)
+        obj_dropped = (self.obj[2] < (self._init_z + 0.005)) and (reach_dist > 0.02)
+
+        # Reach
+        if reach_dist < 0.05:
+            reach_reward = -reach_dist + max(self._action[-1], 0) / 50
+        elif reach_dist_xy < 0.05:
+            reach_reward = -reach_dist
+        else:
+            z_bonus = np.linalg.norm(np.linalg.norm(self.obj[-1] - self.eef[-1]))
+            reach_reward = -reach_dist - 2 * z_bonus
+
+        # Pick
+        if pick_completed and not obj_dropped:
+            pick_reward = self.z_target
+        elif (reach_dist < 0.1) and (self.obj[2] > (self._init_z + 0.005)):
+            pick_reward = min(self.z_target, self.obj[2])
+        else:
+            pick_reward = 0
+
+        return reach_reward / 100 + pick_reward
+
+    def _get_obs(self):
+        eef_velp = self._utils.get_site_xvelp(self.model, self.data, "grasp") * self.dt
+        gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
+        eef = self.eef - self.center_of_table
+
+        obj = self.obj - self.center_of_table
+        obj_rot = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")[-4:]
+        obj_velp = self._utils.get_site_xvelp(self.model, self.data, "object_site") * self.dt
+        obj_velr = self._utils.get_site_xvelr(self.model, self.data, "object_site") * self.dt
+
+        obs = np.concatenate(
+            [
+                eef,
+                eef_velp,
+                obj,
+                obj_rot,
+                obj_velp,
+                obj_velr,
+                eef - obj,
+                np.array(
+                    [
+                        np.linalg.norm(eef - obj),
+                        np.linalg.norm(eef[:-1] - obj[:-1]),
+                        self.z_target,
+                        self.z_target - obj[-1],
+                        self.z_target - eef[-1],
+                    ]
+                ),
+                gripper_angle,
+            ],
+            axis=0,
+        )
+        return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": eef}
+
+    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)
+
+        # Object
+        object_pos = self.center_of_table - np.array([0.15, 0.10, 0.07])
+        object_pos[0] += self.np_random.uniform(-0.05, 0.05, size=1)
+        object_pos[1] += self.np_random.uniform(-0.05, 0.05, size=1)
+        object_qpos = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")
+        object_qpos[:3] = object_pos
+        self._utils.set_joint_qpos(self.model, self.data, "object_joint0", object_qpos)
+        self._init_z = object_pos[2]
+
+        # Goal
+        return object_pos + np.array([0, 0, self._z_threshold])
+
+    def reset(self):
+        self._action = np.zeros(4)
+        return super().reset()
+
+    def step(self, action):
+        self._action = action.copy()
+        return super().step(action)