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51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import numpy as np
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from gym.envs.mujoco import mujoco_env
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from gym import utils
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def mass_center(model):
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mass = model.body_mass
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xpos = model.data.xipos
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return (np.sum(mass * xpos, 0) / np.sum(mass))[0]
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class HumanoidStandupEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self):
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mujoco_env.MujocoEnv.__init__(self, 'humanoidstandup.xml', 5)
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utils.EzPickle.__init__(self)
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def _get_obs(self):
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data = self.model.data
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return np.concatenate([data.qpos.flat[2:],
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data.qvel.flat,
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data.cinert.flat,
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data.cvel.flat,
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data.qfrc_actuator.flat,
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data.cfrc_ext.flat])
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def _step(self, a):
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self.do_simulation(a, self.frame_skip)
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pos_after = self.model.data.qpos[2][0]
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data = self.model.data
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uph_cost = (pos_after - 0) / self.model.opt.timestep
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quad_ctrl_cost = 0.1 * np.square(data.ctrl).sum()
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quad_impact_cost = .5e-6 * np.square(data.cfrc_ext).sum()
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quad_impact_cost = min(quad_impact_cost, 10)
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reward = uph_cost - quad_ctrl_cost - quad_impact_cost + 1
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done = bool(False)
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return self._get_obs(), reward, done, dict(reward_linup=uph_cost, reward_quadctrl=-quad_ctrl_cost, reward_impact=-quad_impact_cost)
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def reset_model(self):
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c = 0.01
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self.set_state(
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self.init_qpos + self.np_random.uniform(low=-c, high=c, size=self.model.nq),
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self.init_qvel + self.np_random.uniform(low=-c, high=c, size=self.model.nv,)
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)
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return self._get_obs()
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 1
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self.viewer.cam.distance = self.model.stat.extent * 1.0
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self.viewer.cam.lookat[2] += .8
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self.viewer.cam.elevation = -20
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