gym_xarm/tasks/push.py

# import numpy as np

from gym_xarm.tasks import Base


class Push(Base):
    """DEPRECATED: use only Lift for now"""

    ...
    # def __init__(self):
    #     super().__init__("push")

    # def _reset_sim(self):
    #     self._act_magnitude = 0
    #     super()._reset_sim()

    # def is_success(self):
    #     return np.linalg.norm(self.obj - self.goal) <= 0.05

    # def get_reward(self):
    #     dist = np.linalg.norm(self.obj - 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

    #     obj = self.obj - self.center_of_table
    #     obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
    #     obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
    #     obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt

    #     obs = np.concatenate(
    #         [
    #             eef,
    #             eef_velp,
    #             goal,
    #             obj,
    #             obj_rot,
    #             obj_velp,
    #             obj_velr,
    #             eef - goal,
    #             eef - obj,
    #             obj - goal,
    #             np.array(
    #                 [
    #                     np.linalg.norm(eef - goal),
    #                     np.linalg.norm(eef - obj),
    #                     np.linalg.norm(obj - 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)

    #     # Object
    #     object_pos = self.center_of_table - np.array([0.25, 0, 0.07])
    #     object_pos[0] += self.np_random.uniform(-0.08, 0.08, size=1)
    #     object_pos[1] += self.np_random.uniform(-0.08, 0.08, size=1)
    #     object_qpos = self.sim.data.get_joint_qpos("object_joint0")
    #     object_qpos[:3] = object_pos
    #     self.sim.data.set_joint_qpos("object_joint0", object_qpos)

    #     # Goal
    #     self.goal = np.array([1.600, 0.200, 0.545])
    #     self.goal[:2] += self.np_random.uniform(-0.1, 0.1, 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)