def render(self, mode='human', close=False):
if mode == 'human':
if self.viewer is None:
self.viewer = MjViewer(self.sim)
self.viewer.render()
elif mode == 'rgb_array':
if self.rgb_viewer is None:
self.rgb_viewer = MjRenderContextOffscreen(self.sim, 0)
self.rgb_viewer.render(500, 500)
# window size used for old mujoco-py:
data = self.rgb_viewer.read_pixels(500, 500, depth=False)
# original image is upside-down, so flip it
return data[::-1, :, :]
def setrendermode(self,rendersetting):#render_flg, plt_switch, screenwidth = 500, screenhight = 500, interval = 5):
self.rendermode["rendertype"]=rendersetting["render_flg"]
if rendersetting["render_flg"] == True: # in-screen: ignore other mode parameters
self.viewer = MjViewer(self.sim)
return
else: # off-screen:
self.rendermode["W"] = rendersetting["screenwidth"]
self.rendermode["H"] = rendersetting["screenhight"]
self.rendermode["pltswitch"] = rendersetting["plt_switch"]
self.viewer = MjRenderContextOffscreen(self.sim)
if plt.get_fignums():
plt.close()
self.fig,self.ax = plt.subplots()
return
def _reset_internal(self):
"""Resets simulation internal configurations."""
# create visualization screen or renderer
if self.has_renderer and self.viewer is None:
self.viewer = MujocoPyRenderer(self.sim)
self.viewer.viewer.vopt.geomgroup[0] = (1 if self.render_collision_mesh else 0)
self.viewer.viewer.vopt.geomgroup[1] = (1 if self.render_visual_mesh else 0)
# hiding the overlay speeds up rendering significantly
self.viewer.viewer._hide_overlay = True
# make sure mujoco-py doesn't block rendering frames
# (see https://github.com/StanfordVL/robosuite/issues/39)
self.viewer.viewer._render_every_frame = True
# Set the camera angle for viewing
if self.render_camera is not None:
self.viewer.set_camera(camera_id=self.sim.model.camera_name2id(self.render_camera))
elif self.has_offscreen_renderer:
if self.sim._render_context_offscreen is None:
render_context = MjRenderContextOffscreen(self.sim, device_id=self.render_gpu_device_id)
self.sim.add_render_context(render_context)
self.sim._render_context_offscreen.vopt.geomgroup[0] = (1 if self.render_collision_mesh else 0)
self.sim._render_context_offscreen.vopt.geomgroup[1] = (1 if self.render_visual_mesh else 0)
# additional housekeeping
self.sim_state_initial = self.sim.get_state()
self._get_reference()
self.cur_time = 0
self.timestep = 0
self.done = False
def _get_sim(self):
if self._sim is not None:
return self._sim
xml = postprocess_model_xml(self._traj.config_str)
self._depth_norm = None
if 'sawyer' in xml:
from hem.datasets.precompiled_models.sawyer import models
self._sim = models[0]
self._depth_norm = 'sawyer'
elif 'baxter' in xml:
from hem.datasets.precompiled_models.baxter import models
self._sim = models[0]
elif 'panda' in xml:
from hem.datasets.precompiled_models.panda import models
self._sim = models[0]
else:
model = load_model_from_xml(xml)
model.vis.quality.offsamples = 8
sim = MjSim(load_model_from_xml(xml))
render_context = MjRenderContextOffscreen(sim)
render_context.vopt.geomgroup[0] = 0
render_context.vopt.geomgroup[1] = 1
sim.add_render_context(render_context)
self._sim = sim
return self._sim
def _reset_internal(self):
"""Resets simulation internal configurations."""
# instantiate simulation from MJCF model
self._load_model()
self.mjpy_model = self.model.get_model(mode="mujoco_py")
self.sim = MjSim(self.mjpy_model)
self.initialize_time(self.control_freq) #设置时间步长
# create visualization screen or renderer
if self.has_renderer and self.viewer is None:
self.viewer = MujocoPyRenderer(self.sim)
self.viewer.viewer.vopt.geomgroup[0] = (
1 if self.render_collision_mesh else 0)
self.viewer.viewer.vopt.geomgroup[
1] = 1 if self.render_visual_mesh else 0
# hiding the overlay speeds up rendering significantly
self.viewer.viewer._hide_overlay = True
elif self.has_offscreen_renderer:
if self.sim._render_context_offscreen is None:
render_context = MjRenderContextOffscreen(self.sim)
self.sim.add_render_context(render_context)
self.sim._render_context_offscreen.vopt.geomgroup[0] = (
1 if self.render_collision_mesh else 0)
self.sim._render_context_offscreen.vopt.geomgroup[1] = (
1 if self.render_visual_mesh else 0)
# additional housekeeping
self.sim_state_initial = self.sim.get_state()
self._get_reference()
self.cur_time = 0
self.timestep = 0
self.done = False
def reset_from_xml_string(self, xml_string):
"""Reloads the environment from an XML description of the environment."""
# if there is an active viewer window, destroy it
self.close()
# load model from xml
self.mjpy_model = load_model_from_xml(xml_string)
self.sim = MjSim(self.mjpy_model)
self.initialize_time(self.control_freq)
if self.has_renderer and self.viewer is None:
self.viewer = MujocoPyRenderer(self.sim)
self.viewer.viewer.vopt.geomgroup[0] = (
1 if self.render_collision_mesh else 0
)
self.viewer.viewer.vopt.geomgroup[1] = 1 if self.render_visual_mesh else 0
# hiding the overlay speeds up rendering significantly
self.viewer.viewer._hide_overlay = True
elif self.has_offscreen_renderer:
render_context = MjRenderContextOffscreen(self.sim)
render_context.vopt.geomgroup[0] = 1 if self.render_collision_mesh else 0
render_context.vopt.geomgroup[1] = 1 if self.render_visual_mesh else 0
self.sim.add_render_context(render_context)
self.sim_state_initial = self.sim.get_state()
self._get_reference()
self.cur_time = 0
self.timestep = 0
self.done = False
# necessary to refresh MjData
self.sim.forward()
def sample_trajectory(env,
policy,
max_path_length,
render=False,
render_mode=("rgb_array")):
# TODO: get this from hw1
# initialize env for the beginning of a new rollout
ob = env.reset() # HINT: should be the output of resetting the env
global viewer
if render and viewer is None and hasattr(env, "sim"):
viewer = MjRenderContextOffscreen(env.sim, 0)
# init vars
obs, acs, rewards, next_obs, terminals, image_obs = [], [], [], [], [], []
steps = 0
while True:
# render image of the simulated env
if render:
if "rgb_array" in render_mode:
if hasattr(env, "sim"):
try:
image_ob = env.sim.render(camera_name="track",
height=500,
width=500)
except:
image_ob = env.sim.render(height=500, width=500)
image_obs.append(image_ob[::-1])
else:
image_obs.append(env.render(mode=render_mode))
if "human" in render_mode:
env.render(mode=render_mode)
time.sleep(env.model.opt.timestep)
# use the most recent ob to decide what to do
obs.append(ob)
ac = policy.get_action(
ob) # HINT: query the policy's get_action function
ac = ac[0]
acs.append(ac)
# take that action and record results
ob, rew, done, _ = env.step(ac)
# record result of taking that action
steps += 1
next_obs.append(ob)
rewards.append(rew)
# TODO end the rollout if the rollout ended
# HINT: rollout can end due to done, or due to max_path_length
rollout_done = done or steps >= max_path_length # HINT: this is either 0 or 1
terminals.append(rollout_done)
if rollout_done:
break
return Path(obs, image_obs, acs, rewards, next_obs, terminals)
def create_model(xml):
model = load_model_from_xml(postprocess_model_xml(xml))
model.vis.quality.offsamples = 8
sim = MjSim(model)
render_context = MjRenderContextOffscreen(sim)
render_context.vopt.geomgroup[0] = 0
render_context.vopt.geomgroup[1] = 1
sim.add_render_context(render_context)
return sim
def _get_viewer(self, mode):
self.viewer = self._viewers.get(mode)
if self.viewer is None:
if mode == 'human':
self.viewer = MjViewer(self.sim)
else:
self.viewer = MjRenderContextOffscreen(self.sim, -1)
self.viewer_setup()
self._viewers[mode] = self.viewer
self.viewer_setup()
return self.viewer
def __init__(self, model_path, frame_skip):
if model_path.startswith("/"):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), "assets",
model_path)
if not path.exists(fullpath):
raise IOError("File %s does not exist" % fullpath)
self.frame_skip = frame_skip
self.model = mujoco_py.load_model_from_path(fullpath)
self.sim = mujoco_py.MjSim(self.model)
self.renderer = MjRenderContextOffscreen(self.sim,
device_id=get_gpu_id())
self.x_dim = 84 # 84
self.y_dim = 84 # 84
self.data = self.sim.data
self.viewer = None
self.metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1.0 / self.dt))
}
self.init_qpos = self.sim.data.qpos.ravel().copy()
self.init_qvel = self.sim.data.qvel.ravel().copy()
observation, _reward, done, _info = self.step(np.zeros(self.model.nu))
assert not done
bounds = self.model.actuator_ctrlrange.copy()
low = bounds[:, 0]
high = bounds[:, 1]
self.action_space = spaces.Box(low=low, high=high, dtype=np.float32)
if isinstance(observation, dict):
# obs_space_dict = {}
# for key, value in observation.items():
# obs_space_dict[key] = spaces.Box(-np.inf, np.inf,
# shape=value.shape,
# dtype=value.dtype)
# self.observation_space = spaces.Dict(obs_space_dict)
self.observation_space = {}
for key, value in observation.items():
self.observation_space[key] = spaces.Box(-np.inf,
np.inf,
shape=value.shape,
dtype=value.dtype)
else:
obs_dim = observation.size
high = np.inf * np.ones(obs_dim)
low = -high
self.observation_space = spaces.Box(low, high, dtype=np.float32)
self.seed()
def _viewer_setup(self, rendersetting):
self.rendermode["rendertype"] = rendersetting["render_flg"]
if rendersetting[
"render_flg"] == True: # in-screen: ignore other mode parameters
self.viewer = MjViewer(self.sim)
else: # off-screen:
self.rendermode["W"] = rendersetting["screenwidth"]
self.rendermode["H"] = rendersetting["screenhight"]
self.rendermode["pltswitch"] = rendersetting["plt_switch"]
self.viewer = MjRenderContextOffscreen(self.sim)
if plt.get_fignums():
plt.close()
self.fig, self.ax = plt.subplots()
# body_id = self.sim.model.body_name2id('robot0:gripper_link')
# lookat = self.sim.data.body_xpos[body_id]
# for idx, value in enumerate(lookat):
# self.viewer.cam.lookat[idx] = value
self.viewer.cam.lookat[:] = rendersetting["lookat"]
self.viewer.cam.distance = rendersetting["distance"]
self.viewer.cam.azimuth = rendersetting["azimuth"]
self.viewer.cam.elevation = rendersetting["elevation"]
def _eval_agent(a_n, env, args, image_based=True, cuda=False):
# load model
env = gym.make('FetchReach-v1')
sim = env.sim
viewer = MjRenderContextOffscreen(sim)
# self.viewer.cam.fixedcamid = 3
# self.viewer.cam.type = const.CAMERA_FIXED
viewer.cam.distance = 1.2
viewer.cam.azimuth = 180
viewer.cam.elevation = -25
env.env._viewers['rgb_array'] = viewer
model_path = './test.pt'
loaded_model = new_actor(get_env_params(env))
loaded_model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage))
if cuda:
loaded_model.cuda()
total_success_rate = []
for _ in range(args.n_test_rollouts):
per_success_rate = []
observation = env.reset()
obs = observation['observation']
g = observation['desired_goal']
obs_img = env.render(mode="rgb_array", height=100, width=100)
for _ in range(env._max_episode_steps):
with torch.no_grad():
if image_based:
o_tensor, g_tensor = _preproc_inputs_image(obs_img.copy()[np.newaxis, :], g[np.newaxis, :], cuda)
pi = loaded_model(o_tensor, g_tensor)
else:
input_tensor = self._preproc_inputs(obs, g)
pi = actor_network(input_tensor)
# convert the actions
actions = pi.detach().cpu().numpy().squeeze()
observation_new, _, _, info = env.step(actions)
obs = observation_new['observation']
obs_img = env.render(mode="rgb_array", height=100, width=100)
g = observation_new['desired_goal']
per_success_rate.append(info['is_success'])
total_success_rate.append(per_success_rate)
total_success_rate = np.array(total_success_rate)
local_success_rate = np.mean(total_success_rate[:, -1])
print(local_success_rate)
def __init__(self, model_path, initial_qpos, n_actions, n_substeps):
if model_path.startswith('/'):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), 'assets',
model_path)
if not os.path.exists(fullpath):
raise IOError('File {} does not exist'.format(fullpath))
model = mujoco_py.load_model_from_path(fullpath)
self.sim = mujoco_py.MjSim(model, nsubsteps=n_substeps)
self.viewer = None
self.renderer = MjRenderContextOffscreen(self.sim, device_id=1)
self.metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': int(np.round(1.0 / self.dt))
}
self.seed()
self._env_setup(initial_qpos=initial_qpos)
self.initial_state = copy.deepcopy(self.sim.get_state())
self.goal = self._sample_goal()
obs = self._get_obs()
self.action_space = spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32')
self.observation_space = spaces.Dict(
dict(
desired_goal=spaces.Box(-np.inf,
np.inf,
shape=obs['achieved_goal'].shape,
dtype='float32'),
achieved_goal=spaces.Box(-np.inf,
np.inf,
shape=obs['achieved_goal'].shape,
dtype='float32'),
observation=spaces.Box(-np.inf,
np.inf,
shape=obs['observation'].shape,
dtype='float32'),
))
lightid = model.light_name2id(name)
value = model.light_ambient[lightid]
print(value)
model.light_ambient[lightid] = value + 1
# model = load_model_from_path("/Users/karanchahal/projects/mujoco-py/xmls/fetch/main.xml")
# model = load_model_from_path("/Users/karanchahal/miniconda3/envs/rlkit/lib/python3.6/site-packages/gym/envs/robotics/assets/fetch/pick_and_place.xml")
# sim = MjSim(model)
env = gym.make('FetchPickAndPlace-v1')
# exit()
# env.sim = sim
sim = env.sim
viewer = MjRenderContextOffscreen(sim)
viewer.cam.fixedcamid = 3
viewer.cam.type = const.CAMERA_FIXED
env.env._viewers['rgb_array'] = viewer
im = env.render(mode="rgb_array")
plt.imshow(im)
plt.show()
modder = TextureModder(sim)
# modder = CameraModder(sim)
modder.whiten_materials()
# modder = MaterialModder(sim)
t = 1
# viewer.cam.fixedcamid = 3
# camera #2
vieweroff.render(width, height, camera_id=1) # if camera_id=None, camera is not rendered
rgb = vieweroff.read_pixels(width, height)[0]
bgr = np.flipud(cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR))
cv2.imshow('teste2', bgr)
cv2.waitKey(1)
model = mujoco_py.load_model_from_path("./assets/my-model-rrr.xml")
''' ATTENTION: if you choose to use Mujoco's default viewer, you can't see the rendering of the cameras!'''
sim = MjSim(model)
vieweroff = MjRenderContextOffscreen(sim,0)
# controller and simulation params
t = 0
qpos_ref = np.array([-2, -1, 2])
qvel_ref = np.array([0, 0, 0])
kp = 1000
kv = 500
sim.model.opt.gravity[:] = np.array([0, 0, 0]) # just to make simulation easier :)
width, height = 800, 480
t_ini = time.time()
try:
class ImiRob:
""" imitate robot that imitates(replay) the actions extracted from other robots
This robot simulator is of the quadruped robob which has 4 legs along with 2 hinge joints for each leg.
This simulation is based on mujoco. All robots are derivation of OpenAI gym ant.
"""
def __init__(self,rendersetting,frame_skip=4,objnum=4,showarm=False):
if showarm:
xmlsource = '/home/cml/CML/MjPushData/xmls/show_env.xml'
else:
xmlsource = '/home/cml/CML/MjPushData/xmls/lab_env.xml'
self.model = load_model_from_path(xmlsource)
self.sim = MjSim(self.model)
self.frame_skip = frame_skip
self.rendermode = {}
self.init_state = self.sim.get_state()
self.viewer = None
self.setrendermode(rendersetting)
self.stopflag = False
self.objnum = objnum
self.showarm = showarm
self.cubes = [TableObj(["free_x_"+str(i+1),"free_y_"+str(i+1)],self.model) for i in range(self.objnum)]
self.move_dict = {
0:[0.3,0],
1:[-0.3,0],
2:[0,0.3,],
3:[0,-0.3,]
}
def init_cubes(self,mjqpos):
for i in range(self.objnum):
pos = [np.random.uniform(low=-1.0, high=1.0),np.random.uniform(low=-1.0, high=1.0)]
# pos =[i*0.3,i*0.3]
mjqpos = self.cubes[i].set_pos(pos,mjqpos)
return mjqpos
def mesh_init_cubes(self,mjqpos,gridsize=0.5):
def mesh_random(pt_num,swing=0.5,seglow=-1.0,seghigh=1.0):
res = []
last_pt = seglow - swing
for remain_pt_num in reversed(range(pt_num)):
segrange = [last_pt+swing,seghigh - remain_pt_num*swing]
last_pt = np.random.uniform(low=segrange[0],high=segrange[1])
res.append(last_pt)
return np.random.permutation(res)
self.x_coord = mesh_random(self.objnum,swing=gridsize)
self.y_coord = mesh_random(self.objnum,swing=gridsize)
for i in range(self.objnum):
pos = [self.x_coord[i],self.y_coord[i]]
mjqpos = self.cubes[i].set_pos(pos,mjqpos)
return mjqpos
def check_contacts(self):
def checkdistance(poses,scaling=0.6,eps=0.1):
poses = sorted(poses)
dis = np.array([np.abs(poses[i]-poses[i+1]) for i in range(len(poses)-1)])
return np.any(dis < eps)
cube_pos_x = [self.cubes[i].pos[0] for i in range(self.objnum)]
cube_pos_y = [self.cubes[i].pos[1] for i in range(self.objnum)]
return checkdistance(cube_pos_x) or checkdistance(cube_pos_y)
def setrendermode(self,rendersetting):#render_flg, plt_switch, screenwidth = 500, screenhight = 500, interval = 5):
self.rendermode["rendertype"]=rendersetting["render_flg"]
if rendersetting["render_flg"] == True: # in-screen: ignore other mode parameters
self.viewer = MjViewer(self.sim)
return
else: # off-screen:
self.rendermode["W"] = rendersetting["screenwidth"]
self.rendermode["H"] = rendersetting["screenhight"]
self.rendermode["pltswitch"] = rendersetting["plt_switch"]
self.viewer = MjRenderContextOffscreen(self.sim)
if plt.get_fignums():
plt.close()
self.fig,self.ax = plt.subplots()
return
def onscreenshow(self):
self.viewer.render()
def offscreenshow(self):
self.viewer.render(self.rendermode["H"],self.rendermode["W"])
im_src = self.viewer.read_pixels(self.rendermode["H"],self.rendermode["W"],depth=False)
im_src = np.flip(im_src)
im_src = np.flip(im_src, axis = 1)
im_src = np.flip(im_src, axis = 2)
if self.rendermode["pltswitch"]:
self.ax.cla()
self.ax.imshow(im_src)
plt.pause(1e-10)
return im_src, not plt.get_fignums()
def set_state(self, qpos, qvel):
assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)
old_state = self.sim.get_state()
new_state = MjSimState(old_state.time, qpos, qvel,
old_state.act, old_state.udd_state)
self.sim.set_state(new_state)
self.sim.forward()
def reset(self):
self.sim.set_state(self.init_state)
self.stopflag = False
self.qposrecrd = []
self.init_qpos = self.sim.data.qpos.ravel().copy()
self.init_qvel = self.sim.data.qvel.ravel().copy()
qpos = self.init_qpos.copy()
qvel = self.init_qvel.copy()
if self.showarm:
qpos = np.array([0.2, -90 / 180 * math.pi, 70 / 180 * math.pi,
0, 0, 0, # robotic arm
0, 0, 0, 0,
0, 0, 0, 0, # two fingers
-0.62, 0.32,
-0.72, 0.38,
-0.835, 0.425,
-0.935, 0.46,]) # 4 cubes
qpos = self.mesh_init_cubes(qpos)
self.set_state(qpos, qvel)
def setcam(self, distance=3, azimuth=0, elevation=-90, lookat=[-0.2,0,0], hideoverlay=True,trackbodyid=-1):
self.viewer.cam.trackbodyid = trackbodyid
self.viewer.cam.distance=distance
self.viewer.cam.azimuth=azimuth
self.viewer.cam.elevation=elevation
self.viewer.cam.lookat[:]=lookat
self.viewer._hide_overlay=hideoverlay
def moverobot(self):
self.init_qpos = self.sim.data.qpos.ravel().copy()
self.init_qvel = self.sim.data.qvel.ravel().copy()
qpos = self.init_qpos.copy()
qvel = self.init_qvel.copy()
qpos=self.cubes[0].move_cube([0.2,0],qpos)
self.set_state(qpos, qvel)
def random_move_cube(self):
move_fashion, move_cube = np.random.randint(4,size=2)
self.init_qpos = self.sim.data.qpos.ravel().copy()
self.init_qvel = self.sim.data.qvel.ravel().copy()
qpos = self.init_qpos.copy()
qvel = self.init_qvel.copy()
qpos=self.cubes[move_cube].move_cube(self.move_dict[move_fashion],qpos)
self.set_state(qpos, qvel)
return move_cube,move_fashion
def save_video(self,savepath):
video_data = np.zeros((2,self.rendermode["H"],self.rendermode["W"],3)).astype(np.uint8)
im1,_ = self.offscreenshow()
movecube,movefashion = self.random_move_cube()
im2,_ = self.offscreenshow()
video_data[0,...] = im1
video_data[1,...] = im2
np.savez_compressed(savepath,video=video_data,cubeindx=movecube,actionindx=movefashion)
def __del__(self):
del self.viewer
del self.model
del self.sim
class PendulumWithGoals(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 30
}
def __init__(self, goal_reaching_thresholds=np.array([0.075, 0.075, 0.75]),
goal_not_reached_penalty=-1, goal_reached_reward=0, terminate_on_goal_reaching=True,
time_limit=1000, frameskip=1, random_goals_instead_of_standing_goal=False,
polar_coordinates: bool=False):
super().__init__()
dir = os.path.dirname(__file__)
model = load_model_from_path(dir + "/pendulum_with_goals.xml")
self.sim = MjSim(model)
self.viewer = None
self.rgb_viewer = None
self.frameskip = frameskip
self.goal = None
self.goal_reaching_thresholds = goal_reaching_thresholds
self.goal_not_reached_penalty = goal_not_reached_penalty
self.goal_reached_reward = goal_reached_reward
self.terminate_on_goal_reaching = terminate_on_goal_reaching
self.time_limit = time_limit
self.current_episode_steps_counter = 0
self.random_goals_instead_of_standing_goal = random_goals_instead_of_standing_goal
self.polar_coordinates = polar_coordinates
# spaces definition
self.action_space = spaces.Box(low=-self.sim.model.actuator_ctrlrange[:, 1],
high=self.sim.model.actuator_ctrlrange[:, 1],
dtype=np.float32)
if self.polar_coordinates:
self.observation_space = spaces.Dict({
"observation": spaces.Box(low=np.array([-np.pi, -15]),
high=np.array([np.pi, 15]),
dtype=np.float32),
"desired_goal": spaces.Box(low=np.array([-np.pi, -15]),
high=np.array([np.pi, 15]),
dtype=np.float32),
"achieved_goal": spaces.Box(low=np.array([-np.pi, -15]),
high=np.array([np.pi, 15]),
dtype=np.float32)
})
else:
self.observation_space = spaces.Dict({
"observation": spaces.Box(low=np.array([-1, -1, -15]),
high=np.array([1, 1, 15]),
dtype=np.float32),
"desired_goal": spaces.Box(low=np.array([-1, -1, -15]),
high=np.array([1, 1, 15]),
dtype=np.float32),
"achieved_goal": spaces.Box(low=np.array([-1, -1, -15]),
high=np.array([1, 1, 15]),
dtype=np.float32)
})
self.spec = EnvSpec('PendulumWithGoals-v0')
self.spec.reward_threshold = self.goal_not_reached_penalty * self.time_limit
self.reset()
def _goal_reached(self):
observation = self._get_obs()
if np.any(np.abs(observation['achieved_goal'] - observation['desired_goal']) > self.goal_reaching_thresholds):
return False
else:
return True
def _terminate(self):
if (self._goal_reached() and self.terminate_on_goal_reaching) or \
self.current_episode_steps_counter >= self.time_limit:
return True
else:
return False
def _reward(self):
if self._goal_reached():
return self.goal_reached_reward
else:
return self.goal_not_reached_penalty
def step(self, action):
self.sim.data.ctrl[:] = action
for _ in range(self.frameskip):
self.sim.step()
self.current_episode_steps_counter += 1
state = self._get_obs()
# visualize the angular velocities
state_velocity = np.copy(state['observation'][-1] / 20)
goal_velocity = self.goal[-1] / 20
self.sim.model.site_size[2] = np.array([0.01, 0.01, state_velocity])
self.sim.data.mocap_pos[2] = np.array([0.85, 0, 0.75 + state_velocity])
self.sim.model.site_size[3] = np.array([0.01, 0.01, goal_velocity])
self.sim.data.mocap_pos[3] = np.array([1.15, 0, 0.75 + goal_velocity])
return state, self._reward(), self._terminate(), {}
def _get_obs(self):
"""
y
^
|____
| /
| /
|~/
|/
--------> x
"""
# observation
angle = self.sim.data.qpos
angular_velocity = self.sim.data.qvel
if self.polar_coordinates:
observation = np.concatenate([angle - np.pi, angular_velocity])
else:
x = np.sin(angle)
y = np.cos(angle) # qpos is the angle relative to a standing pole
observation = np.concatenate([x, y, angular_velocity])
return {
"observation": observation,
"desired_goal": self.goal,
"achieved_goal": observation
}
def reset(self):
self.current_episode_steps_counter = 0
# set initial state
angle = np.random.uniform(np.pi / 4, 7 * np.pi / 4)
angular_velocity = np.random.uniform(-0.05, 0.05)
self.sim.data.qpos[0] = angle
self.sim.data.qvel[0] = angular_velocity
self.sim.step()
# goal
if self.random_goals_instead_of_standing_goal:
angle_target = np.random.uniform(-np.pi / 8, np.pi / 8)
angular_velocity_target = np.random.uniform(-0.2, 0.2)
else:
angle_target = 0
angular_velocity_target = 0
# convert target values to goal
x_target = np.sin(angle_target)
y_target = np.cos(angle_target)
if self.polar_coordinates:
self.goal = np.array([angle_target - np.pi, angular_velocity_target])
else:
self.goal = np.array([x_target, y_target, angular_velocity_target])
# visualize the goal
self.sim.data.mocap_pos[0] = [x_target, 0, y_target]
return self._get_obs()
def render(self, mode='human', close=False):
if mode == 'human':
if self.viewer is None:
self.viewer = MjViewer(self.sim)
self.viewer.render()
elif mode == 'rgb_array':
if self.rgb_viewer is None:
self.rgb_viewer = MjRenderContextOffscreen(self.sim, 0)
self.rgb_viewer.render(500, 500)
# window size used for old mujoco-py:
data = self.rgb_viewer.read_pixels(500, 500, depth=False)
# original image is upside-down, so flip it
return data[::-1, :, :]
has_offscreen_renderer=False, # not needed since not using pixel obs
has_renderer=False, # make sure we can render to the screen
reward_shaping=True, # use dense rewards
control_freq=
30, # control should happen fast enough so that simulation looks smooth
)
world.mode = 'human'
world.reset()
print(dir(world.mujoco_arena))
print(world.mujoco_arena.table_full_size)
print(world.mujoco_arena.bin_abs)
print(type(world.sim))
sim = world.sim
print(dir(sim))
viewer = MjRenderContextOffscreen(sim, 0)
print(dir(viewer.scn))
set_camera_birdview(viewer)
width, height = 516, 386
viewer.render(width, height)
image = np.asarray(viewer.read_pixels(width, height, depth=False)[:, :, :],
dtype=np.uint8)
depth = np.asarray((viewer.read_pixels(width, height, depth=True)[1]))
# find center depth, this is a hack
cdepth = depth[height // 2, width // 2]
print(cdepth)
depth[depth > cdepth] = cdepth
seg = depth != cdepth
seg[:, :padding], seg[:, -padding:] = False, False
seg[:t_padding, :], seg[-t_padding:, :] = False, False
cv2.imwrite('test_dataset/seg.png', seg * 255)
model_path = './test.pt'
model = torch.load(model_path, map_location=lambda storage, loc: storage)
# create the environment
env = gym.make(args.env_name)
# get the env param
observation = env.reset()
# get the environment params
env_params = {'obs': observation['observation'].shape[0],
'goal': observation['desired_goal'].shape[0],
'action': env.action_space.shape[0],
'action_max': env.action_space.high[0],
}
if image_based:
sim = env.sim
viewer = MjRenderContextOffscreen(sim)
viewer.cam.fixedcamid = 3
viewer.cam.type = const.CAMERA_FIXED
env.env._viewers['rgb_array'] = viewer
# create the actor network
actor_network = new_actor(env_params)
actor_network.load_state_dict(model)
# actor_network.eval()
_eval_agent(actor_network, env, args)
exit()
for i in range(args.demo_length):
observation = env.reset()
# start to do the demo
obs = observation['observation']
g = observation['desired_goal']
class FetchPushEnv(FetchEnv):
def __init__(self, rendersetting, objnum=4, reward_type='sparse'):
initial_qpos = {
'robot0:slide0': 0.405,
'robot0:slide1': 0.48,
'robot0:slide2': 0.0,
}
FetchEnv.__init__(self,
MODEL_XML_PATH,
has_object=True,
block_gripper=True,
n_substeps=20,
gripper_extra_height=0.0,
target_in_the_air=False,
target_offset=0.0,
obj_range=0.15,
target_range=0.15,
distance_threshold=0.05,
initial_qpos=initial_qpos,
reward_type=reward_type)
utils.EzPickle.__init__(self)
self.rendermode = {}
self._viewer_setup(rendersetting)
self.objnum = objnum
self.objs = [
TableObj("object{}:joint".format(i), self.model)
for i in range(self.objnum)
]
self.movedict = {
"dimidx": [
np.array([1.0, 0.0]), #x - pos
np.array([-1.0, 0.0]), #x - neg
np.array([0.0, 1.0]), #y - pos,
np.array([0.0, -1.0])
], #y - neg
}
def _viewer_setup(self, rendersetting):
self.rendermode["rendertype"] = rendersetting["render_flg"]
if rendersetting[
"render_flg"] == True: # in-screen: ignore other mode parameters
self.viewer = MjViewer(self.sim)
else: # off-screen:
self.rendermode["W"] = rendersetting["screenwidth"]
self.rendermode["H"] = rendersetting["screenhight"]
self.rendermode["pltswitch"] = rendersetting["plt_switch"]
self.viewer = MjRenderContextOffscreen(self.sim)
if plt.get_fignums():
plt.close()
self.fig, self.ax = plt.subplots()
# body_id = self.sim.model.body_name2id('robot0:gripper_link')
# lookat = self.sim.data.body_xpos[body_id]
# for idx, value in enumerate(lookat):
# self.viewer.cam.lookat[idx] = value
self.viewer.cam.lookat[:] = rendersetting["lookat"]
self.viewer.cam.distance = rendersetting["distance"]
self.viewer.cam.azimuth = rendersetting["azimuth"]
self.viewer.cam.elevation = rendersetting["elevation"]
def offscreenrender(self):
self.viewer.render(self.rendermode["H"], self.rendermode["W"])
im_src = self.viewer.read_pixels(self.rendermode["H"],
self.rendermode["W"],
depth=False)
im_src = np.flip(im_src)
im_src = np.flip(im_src, axis=1)
im_src = np.flip(im_src, axis=2)
if self.rendermode["pltswitch"]:
self.ax.cla()
self.ax.imshow(im_src)
plt.pause(1e-10)
return im_src, not plt.get_fignums()
def move_gripper(self, newpos):
# Move end effector into position.
gripper_target = np.array(newpos)
gripper_rotation = np.array([1., 0., 1., 0.])
self.sim.data.set_mocap_pos('robot0:mocap', gripper_target)
self.sim.data.set_mocap_quat('robot0:mocap', gripper_rotation)
for _ in range(10):
self.sim.step()
self.viewer.render()
# self.offscreenrender()
def mesh_init_cubes(self, gridsize=0.4):
def mesh_random(pt_num, swing=0.375, seglow=-0.7, seghigh=0.8):
res = []
last_pt = seglow - swing
for remain_pt_num in reversed(range(pt_num)):
segrange = [last_pt + swing, seghigh - remain_pt_num * swing]
last_pt = np.random.uniform(low=segrange[0], high=segrange[1])
res.append(last_pt)
return np.random.permutation(res)
self.x_coord = mesh_random(self.objnum, swing=gridsize)
self.y_coord = mesh_random(self.objnum, swing=gridsize)
for i in range(self.objnum):
pos = [self.x_coord[i], self.y_coord[i]]
self.objs[i].set_pos(pos, self.sim)
self.sim.forward()
def random_push(self, savepath):
video_data = np.zeros((2, self.rendermode["H"], self.rendermode["W"],
3)).astype(np.uint8)
move_fashion, move_cube = np.random.randint(4, size=2)
im1, _ = self.offscreenrender()
self.push_obj(move_fashion, move_cube)
im2, _ = self.offscreenrender()
video_data[0, ...] = im1
video_data[1, ...] = im2
np.savez_compressed(savepath,
video=video_data,
cubeindx=move_cube,
actionindx=move_fashion)
def push_obj(self, move_fashion, move_cube):
objpos = np.array(self.objs[move_cube].get_pos())
dimidx = self.movedict["dimidx"][move_fashion]
objpos += np.append(dimidx * 0.06, [0.4])
# objpos[1]+= 0.07
# objpos[2]+= 0.4
self.move_gripper(objpos)
objpos[2] -= 0.38
self.move_gripper(objpos)
for _ in range(10):
self._set_action(np.append(dimidx * -0.4, [0.0, 0.0]))
# self._set_action(np.array([0.0,-0.4,0,0]))
self.sim.step()
# self.viewer.render()
# self.offscreenrender()
for _ in range(10):
self._set_action(np.append(dimidx * 0.1, [0.0, 0.0]))
# self._set_action(np.array([0.0,0.1,0,0]))
self.sim.step()
self.viewer.render()
# self.offscreenrender()
objpos[2] += 0.7
self.move_gripper(objpos)
def reset(self):
self._reset_sim()
self.mesh_init_cubes()
self.move_gripper([1.2, 0.75, 0.8])
def build(self):
''' Build a world, including generating XML and moving objects '''
# Read in the base XML (contains robot, camera, floor, etc)
self.robot_base_path = os.path.join(BASE_DIR, self.robot_base)
with open(self.robot_base_path) as f:
self.robot_base_xml = f.read()
self.xml = xmltodict.parse(
self.robot_base_xml) # Nested OrderedDict objects
# Convenience accessor for xml dictionary
worldbody = self.xml['mujoco']['worldbody']
# Move robot position to starting position
worldbody['body']['@pos'] = convert(np.r_[self.robot_xy,
self.robot.z_height])
worldbody['body']['@quat'] = convert(rot2quat(self.robot_rot))
# We need this because xmltodict skips over single-item lists in the tree
worldbody['body'] = [worldbody['body']]
if 'geom' in worldbody:
worldbody['geom'] = [worldbody['geom']]
else:
worldbody['geom'] = []
# Add equality section if missing
if 'equality' not in self.xml['mujoco']:
self.xml['mujoco']['equality'] = OrderedDict()
equality = self.xml['mujoco']['equality']
if 'weld' not in equality:
equality['weld'] = []
# Add asset section if missing
if 'asset' not in self.xml['mujoco']:
# old default rgb1: ".4 .5 .6"
# old default rgb2: "0 0 0"
# light pink: "1 0.44 .81"
# light blue: "0.004 0.804 .996"
# light purple: ".676 .547 .996"
# med blue: "0.527 0.582 0.906"
# indigo: "0.293 0 0.508"
asset = xmltodict.parse('''
<asset>
<texture type="skybox" builtin="gradient" rgb1="0.527 0.582 0.906" rgb2="0.1 0.1 0.35"
width="800" height="800" markrgb="1 1 1" mark="random" random="0.001"/>
<texture name="texplane" builtin="checker" height="100" width="100"
rgb1="0.7 0.7 0.7" rgb2="0.8 0.8 0.8" type="2d"/>
<material name="MatPlane" reflectance="0.1" shininess="0.1" specular="0.1"
texrepeat="10 10" texture="texplane"/>
</asset>
''')
self.xml['mujoco']['asset'] = asset['asset']
# Add light to the XML dictionary
light = xmltodict.parse('''<b>
<light cutoff="100" diffuse="1 1 1" dir="0 0 -1" directional="true"
exponent="1" pos="0 0 0.5" specular="0 0 0" castshadow="false"/>
</b>''')
worldbody['light'] = light['b']['light']
# Add floor to the XML dictionary if missing
if not any(g.get('@name') == 'floor' for g in worldbody['geom']):
floor = xmltodict.parse('''
<geom name="floor" type="plane" condim="6"/>
''')
worldbody['geom'].append(floor['geom'])
# Make sure floor renders the same for every world
for g in worldbody['geom']:
if g['@name'] == 'floor':
g.update({
'@size': convert(self.floor_size),
'@rgba': '1 1 1 1',
'@material': 'MatPlane'
})
# Add cameras to the XML dictionary
cameras = xmltodict.parse('''<b>
<camera name="fixednear" pos="0 -2 2" zaxis="0 -1 1"/>
<camera name="fixedfar" pos="0 -5 5" zaxis="0 -1 1"/>
<camera name="fixedtop" pos="0 0 5" zaxis="0 0 1"/>
</b>''')
worldbody['camera'] = cameras['b']['camera']
# Build and add a tracking camera (logic needed to ensure orientation correct)
theta = self.robot_rot
xyaxes = dict(x1=np.cos(theta),
x2=-np.sin(theta),
x3=0,
y1=np.sin(theta),
y2=np.cos(theta),
y3=1)
pos = dict(xp=0 * np.cos(theta) + (-2) * np.sin(theta),
yp=0 * (-np.sin(theta)) + (-2) * np.cos(theta),
zp=2)
track_camera = xmltodict.parse('''<b>
<camera name="track" mode="track" pos="{xp} {yp} {zp}" xyaxes="{x1} {x2} {x3} {y1} {y2} {y3}"/>
</b>'''.format(**pos, **xyaxes))
# support multiple cameras on 'body'
if not isinstance(worldbody['body'][0]['camera'], list):
worldbody['body'][0]['camera'] = [worldbody['body'][0]['camera']]
worldbody['body'][0]['camera'].append(track_camera['b']['camera'])
# Add objects to the XML dictionary
for name, object in self.objects.items():
assert object['name'] == name, f'Inconsistent {name} {object}'
object = object.copy() # don't modify original object
object['quat'] = rot2quat(object['rot'])
if name == 'box':
dim = object['size'][0]
object['height'] = object['size'][-1]
object['width'] = dim / 2
object['x'] = dim
object['y'] = dim
body = xmltodict.parse(
'''
<body name="{name}" pos="{pos}" quat="{quat}">
<freejoint name="{name}"/>
<geom name="{name}" type="{type}" size="{size}" density="{density}"
rgba="{rgba}" group="{group}"/>
<geom name="col1" type="{type}" size="{width} {width} {height}" density="{density}"
rgba="{rgba}" group="{group}" pos="{x} {y} 0"/>
<geom name="col2" type="{type}" size="{width} {width} {height}" density="{density}"
rgba="{rgba}" group="{group}" pos="-{x} {y} 0"/>
<geom name="col3" type="{type}" size="{width} {width} {height}" density="{density}"
rgba="{rgba}" group="{group}" pos="{x} -{y} 0"/>
<geom name="col4" type="{type}" size="{width} {width} {height}" density="{density}"
rgba="{rgba}" group="{group}" pos="-{x} -{y} 0"/>
</body>
'''.format(**{k: convert(v)
for k, v in object.items()}))
else:
body = xmltodict.parse(
'''
<body name="{name}" pos="{pos}" quat="{quat}">
<freejoint name="{name}"/>
<geom name="{name}" type="{type}" size="{size}" density="{density}"
rgba="{rgba}" group="{group}"/>
</body>
'''.format(**{k: convert(v)
for k, v in object.items()}))
# Append new body to world, making it a list optionally
# Add the object to the world
worldbody['body'].append(body['body'])
# Add mocaps to the XML dictionary
for name, mocap in self.mocaps.items():
# Mocap names are suffixed with 'mocap'
assert mocap['name'] == name, f'Inconsistent {name} {object}'
assert name.replace(
'mocap', 'obj') in self.objects, f'missing object for {name}'
# Add the object to the world
mocap = mocap.copy() # don't modify original object
mocap['quat'] = rot2quat(mocap['rot'])
body = xmltodict.parse('''
<body name="{name}" mocap="true">
<geom name="{name}" type="{type}" size="{size}" rgba="{rgba}"
pos="{pos}" quat="{quat}" contype="0" conaffinity="0" group="{group}"/>
</body>
'''.format(**{k: convert(v)
for k, v in mocap.items()}))
worldbody['body'].append(body['body'])
# Add weld to equality list
mocap['body1'] = name
mocap['body2'] = name.replace('mocap', 'obj')
weld = xmltodict.parse('''
<weld name="{name}" body1="{body1}" body2="{body2}" solref=".02 1.5"/>
'''.format(**{k: convert(v)
for k, v in mocap.items()}))
equality['weld'].append(weld['weld'])
# Add geoms to XML dictionary
for name, geom in self.geoms.items():
assert geom['name'] == name, f'Inconsistent {name} {geom}'
geom = geom.copy() # don't modify original object
geom['quat'] = rot2quat(geom['rot'])
geom['contype'] = geom.get('contype', 1)
geom['conaffinity'] = geom.get('conaffinity', 1)
body = xmltodict.parse('''
<body name="{name}" pos="{pos}" quat="{quat}">
<geom name="{name}" type="{type}" size="{size}" rgba="{rgba}" group="{group}"
contype="{contype}" conaffinity="{conaffinity}"/>
</body>
'''.format(**{k: convert(v)
for k, v in geom.items()}))
# Append new body to world, making it a list optionally
# Add the object to the world
worldbody['body'].append(body['body'])
# Instantiate simulator
# print(xmltodict.unparse(self.xml, pretty=True))
self.xml_string = xmltodict.unparse(self.xml)
self.model = load_model_from_xml(self.xml_string)
self.sim = MjSim(self.model)
# Add render contexts to newly created sim
if self.render_context is None and self.observe_vision:
render_context = MjRenderContextOffscreen(
self.sim, device_id=self.render_device_id, quiet=True)
render_context.vopt.geomgroup[:] = 1
self.render_context = render_context
if self.render_context is not None:
self.render_context.update_sim(self.sim)
# Recompute simulation intrinsics from new position
self.sim.forward()
def __init__(self, args, env, env_params):
self.args = args
self.env = env
self.env_params = env_params
sim = self.env.sim
self.viewer = MjRenderContextOffscreen(sim)
# self.viewer.cam.fixedcamid = 3
# self.viewer.cam.type = const.CAMERA_FIXED
self.critic_loss = []
self.actor_loss = []
self.viewer.cam.distance = 1.2
self.viewer.cam.azimuth = 180
self.viewer.cam.elevation = -25
env.env._viewers['rgb_array'] = self.viewer
self.env_params = env_params
self.image_based = True if args.image else False
print("Training image based RL ? : {}".format(self.image_based))
# create the network
if not self.image_based:
self.actor_network = actor(env_params)
else:
self.actor_network = new_actor(env_params)
#self.actor_network = resnet_actor(env_params)
self.critic_network = critic(env_params)
# sync the networks across the cpus
sync_networks(self.actor_network)
sync_networks(self.critic_network)
# build up the target network
if not self.image_based:
self.actor_target_network = actor(env_params)
else:
#self.actor_target_network = resnet_actor(env_params)
self.actor_target_network = new_actor(env_params)
self.critic_target_network = critic(env_params)
# load the weights into the target networks
self.actor_target_network.load_state_dict(
self.actor_network.state_dict())
self.critic_target_network.load_state_dict(
self.critic_network.state_dict())
# if use gpu
if self.args.cuda:
print("use the GPU")
self.actor_network.cuda(MPI.COMM_WORLD.Get_rank())
self.critic_network.cuda(MPI.COMM_WORLD.Get_rank())
self.actor_target_network.cuda(MPI.COMM_WORLD.Get_rank())
self.critic_target_network.cuda(MPI.COMM_WORLD.Get_rank())
# create the optimizer
self.actor_optim = torch.optim.Adam(self.actor_network.parameters(),
lr=self.args.lr_actor)
self.critic_optim = torch.optim.Adam(self.critic_network.parameters(),
lr=self.args.lr_critic)
# her sampler
self.her_module = her_sampler(self.args.replay_strategy,
self.args.replay_k,
self.env.compute_reward,
self.image_based)
# create the replay buffer
self.buffer = replay_buffer(self.env_params, self.args.buffer_size,
self.her_module.sample_her_transitions,
self.image_based)
# create the normalizer
self.o_norm = normalizer(size=env_params['obs'],
default_clip_range=self.args.clip_range)
self.g_norm = normalizer(size=env_params['goal'],
default_clip_range=self.args.clip_range)
# create the dict for store the model
if MPI.COMM_WORLD.Get_rank() == 0:
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# path to save the model
self.model_path = os.path.join(self.args.save_dir,
self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
