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shadow_hand_lift_underarm.py
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shadow_hand_lift_underarm.py
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
import random
from PIL import Image as Im
from isaacgym import gymapi
from isaacgym import gymtorch
from rofunc.learning.RofuncRL.tasks.isaacgymenv.base.vec_task import VecTask
from rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils import *
from rofunc.utils.oslab import get_rofunc_path
class ShadowHandLiftUnderarmTask(VecTask):
"""
This class corresponds to the LiftUnderarm task. This environment requires grasping the pot handle
with two hands and lifting the pot to the designated position. This environment is designed to
simulate the scene of lift in daily life and is a practical skill.
"""
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture,
force_render, agent_index=[[[0, 1, 2, 3, 4, 5]], [[0, 1, 2, 3, 4, 5]]], is_multi_agent=False):
self.cfg = cfg
self.agent_index = agent_index
self.is_multi_agent = is_multi_agent
self.randomize = self.cfg["task"]["randomize"]
self.randomization_params = self.cfg["task"]["randomization_params"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dist_reward_scale = self.cfg["env"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rotRewardScale"]
self.action_penalty_scale = self.cfg["env"]["actionPenaltyScale"]
self.success_tolerance = self.cfg["env"]["successTolerance"]
self.reach_goal_bonus = self.cfg["env"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["fallPenalty"]
self.rot_eps = self.cfg["env"]["rotEps"]
self.vel_obs_scale = 0.2 # scale factor of velocity based observations
self.force_torque_obs_scale = 10.0 # scale factor of velocity based observations
self.reset_position_noise = self.cfg["env"]["resetPositionNoise"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise = self.cfg["env"]["resetDofPosRandomInterval"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.shadow_hand_dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
self.use_relative_control = self.cfg["env"]["useRelativeControl"]
self.act_moving_average = self.cfg["env"]["actionsMovingAverage"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.print_success_stat = self.cfg["env"]["printNumSuccesses"]
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.av_factor = self.cfg["env"].get("averFactor", 0.01)
print("Averaging factor: ", self.av_factor)
self.transition_scale = self.cfg["env"]["transition_scale"]
self.orientation_scale = self.cfg["env"]["orientation_scale"]
control_freq_inv = self.cfg["env"].get("controlFrequencyInv", 1)
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time / (control_freq_inv * self.sim_params.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
self.object_type = self.cfg["env"]["objectType"]
# assert self.object_type in ["block", "egg", "pen"]
self.ignore_z = (self.object_type == "pen")
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf",
"egg": "mjcf/open_ai_assets/hand/egg.xml",
"pen": "mjcf/open_ai_assets/hand/pen.xml",
# "pot": "mjcf/pot.xml",
"pot": "mjcf/pot/mobility.urdf"
}
if "asset" in self.cfg["env"]:
self.asset_files_dict["block"] = self.cfg["env"]["asset"].get("assetFileNameBlock",
self.asset_files_dict["block"])
self.asset_files_dict["egg"] = self.cfg["env"]["asset"].get("assetFileNameEgg",
self.asset_files_dict["egg"])
self.asset_files_dict["pen"] = self.cfg["env"]["asset"].get("assetFileNamePen",
self.asset_files_dict["pen"])
# can be "openai", "full_no_vel", "full", "full_state"
self.obs_type = self.cfg["env"]["observationType"]
if not (self.obs_type in ["point_cloud", "full_state"]):
raise Exception(
"Unknown type of observations!\nobservationType should be one of: [point_cloud, full_state]")
print("Obs type:", self.obs_type)
self.num_point_cloud_feature_dim = 768
self.num_obs_dict = {
"point_cloud": 417 + self.num_point_cloud_feature_dim * 3,
"point_cloud_for_distill": 417 + self.num_point_cloud_feature_dim * 3,
"full_state": 417
}
self.num_hand_obs = 72 + 95 + 26 + 6
self.up_axis = 'z'
self.fingertips = ["robot0:ffdistal", "robot0:mfdistal", "robot0:rfdistal", "robot0:lfdistal",
"robot0:thdistal"]
self.a_fingertips = ["robot1:ffdistal", "robot1:mfdistal", "robot1:rfdistal", "robot1:lfdistal",
"robot1:thdistal"]
self.hand_center = ["robot1:palm"]
self.num_fingertips = len(self.fingertips) * 2
self.use_vel_obs = False
self.fingertip_obs = True
self.asymmetric_obs = self.cfg["env"]["asymmetric_observations"]
num_states = 0
if self.asymmetric_obs:
num_states = 211
self.cfg["env"]["numObservations"] = self.num_obs_dict[self.obs_type]
self.cfg["env"]["numStates"] = num_states
if self.is_multi_agent:
self.num_agents = 2
self.cfg["env"]["numActions"] = 26
else:
self.num_agents = 1
self.cfg["env"]["numActions"] = 52
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
if self.obs_type in ["point_cloud"]:
from PIL import Image as Im
# from pointnet2_ops import pointnet2_utils
self.camera_debug = self.cfg["env"].get("cameraDebug", False)
self.point_cloud_debug = self.cfg["env"].get("pointCloudDebug", False)
if self.viewer != None:
cam_pos = gymapi.Vec3(10.0, 5.0, 1.0)
cam_target = gymapi.Vec3(6.0, 5.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, self.num_fingertips * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs,
self.num_shadow_hand_dofs * 2 + 2)
self.dof_force_tensor = self.dof_force_tensor[:, :48]
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.shadow_hand_default_dof_pos = torch.zeros(self.num_shadow_hand_dofs, dtype=torch.float, device=self.device)
# self.shadow_hand_default_dof_pos = to_torch([-0.1220, -0.0718, -0.0020, 1.0493, 0.5983, 0.0000, 0.0118, 0.8989,
# 0.6558, 0.0000, -0.0272, 0.5225, 0.8859, 0.0000, 0.3630, -0.1092,
# 0.2343, 1.0410, 0.0000, 0.8897, 0.7647, 0.0219, -0.0569, -1.1214], dtype=torch.float, device=self.device)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.shadow_hand_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, :self.num_shadow_hand_dofs]
self.shadow_hand_dof_pos = self.shadow_hand_dof_state[..., 0]
self.shadow_hand_dof_vel = self.shadow_hand_dof_state[..., 1]
self.shadow_hand_another_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:,
self.num_shadow_hand_dofs:self.num_shadow_hand_dofs * 2]
self.shadow_hand_another_dof_pos = self.shadow_hand_another_dof_state[..., 0]
self.shadow_hand_another_dof_vel = self.shadow_hand_another_dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
self.hand_positions = self.root_state_tensor[:, 0:3]
self.hand_orientations = self.root_state_tensor[:, 3:7]
self.hand_linvels = self.root_state_tensor[:, 7:10]
self.hand_angvels = self.root_state_tensor[:, 10:13]
self.saved_root_tensor = self.root_state_tensor.clone()
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
self.prev_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(self.num_envs,
-1)
self.x_unit_tensor = to_torch([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = to_torch([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = to_torch([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)
self.av_factor = to_torch(self.av_factor, dtype=torch.float, device=self.device)
self.apply_forces = torch.zeros((self.num_envs, self.num_bodies, 3), device=self.device, dtype=torch.float)
self.apply_torque = torch.zeros((self.num_envs, self.num_bodies, 3), device=self.device, dtype=torch.float)
self.total_successes = 0
self.total_resets = 0
def create_sim(self):
"""
Allocates which device will simulate and which device will render the scene. Defines the simulation type to be used
"""
self.dt = self.sim_params.dt
self.up_axis_idx = self.set_sim_params_up_axis(self.sim_params, self.up_axis)
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
"""
Adds ground plane to simulation
"""
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
"""
Create multiple parallel isaacgym environments
Args:
num_envs (int): The total number of environment
spacing (float): Specifies half the side length of the square area occupied by each environment
num_per_row (int): Specify how many environments in a row
"""
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
# get rofunc path from rofunc package metadata
rofunc_path = get_rofunc_path()
asset_root = os.path.join(rofunc_path, "simulator/assets")
shadow_hand_asset_file = "mjcf/open_ai_assets/hand/shadow_hand.xml"
shadow_hand_another_asset_file = "mjcf/open_ai_assets/hand/shadow_hand1.xml"
table_texture_files = os.path.join(asset_root, "textures/texture_stone_stone_texture_0.jpg")
table_texture_handle = self.gym.create_texture_from_file(self.sim, table_texture_files)
if "asset" in self.cfg["env"]:
asset_root = self.cfg["env"]["asset"].get("assetRoot", asset_root)
shadow_hand_asset_file = self.cfg["env"]["asset"].get("assetFileName", shadow_hand_asset_file)
object_asset_file = self.asset_files_dict[self.object_type]
# load shadow hand_ asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = False
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.angular_damping = 100
asset_options.linear_damping = 100
if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
shadow_hand_asset = self.gym.load_asset(self.sim, asset_root, shadow_hand_asset_file, asset_options)
shadow_hand_another_asset = self.gym.load_asset(self.sim, asset_root, shadow_hand_another_asset_file,
asset_options)
self.num_shadow_hand_bodies = self.gym.get_asset_rigid_body_count(shadow_hand_asset)
self.num_shadow_hand_shapes = self.gym.get_asset_rigid_shape_count(shadow_hand_asset)
self.num_shadow_hand_dofs = self.gym.get_asset_dof_count(shadow_hand_asset)
self.num_shadow_hand_actuators = self.gym.get_asset_actuator_count(shadow_hand_asset)
self.num_shadow_hand_tendons = self.gym.get_asset_tendon_count(shadow_hand_asset)
print("self.num_shadow_hand_bodies: ", self.num_shadow_hand_bodies)
print("self.num_shadow_hand_shapes: ", self.num_shadow_hand_shapes)
print("self.num_shadow_hand_dofs: ", self.num_shadow_hand_dofs)
print("self.num_shadow_hand_actuators: ", self.num_shadow_hand_actuators)
print("self.num_shadow_hand_tendons: ", self.num_shadow_hand_tendons)
# tendon set up
limit_stiffness = 30
t_damping = 0.1
relevant_tendons = ["robot0:T_FFJ1c", "robot0:T_MFJ1c", "robot0:T_RFJ1c", "robot0:T_LFJ1c"]
a_relevant_tendons = ["robot1:T_FFJ1c", "robot1:T_MFJ1c", "robot1:T_RFJ1c", "robot1:T_LFJ1c"]
tendon_props = self.gym.get_asset_tendon_properties(shadow_hand_asset)
a_tendon_props = self.gym.get_asset_tendon_properties(shadow_hand_another_asset)
for i in range(self.num_shadow_hand_tendons):
for rt in relevant_tendons:
if self.gym.get_asset_tendon_name(shadow_hand_asset, i) == rt:
tendon_props[i].limit_stiffness = limit_stiffness
tendon_props[i].damping = t_damping
for rt in a_relevant_tendons:
if self.gym.get_asset_tendon_name(shadow_hand_another_asset, i) == rt:
a_tendon_props[i].limit_stiffness = limit_stiffness
a_tendon_props[i].damping = t_damping
self.gym.set_asset_tendon_properties(shadow_hand_asset, tendon_props)
self.gym.set_asset_tendon_properties(shadow_hand_another_asset, a_tendon_props)
actuated_dof_names = [self.gym.get_asset_actuator_joint_name(shadow_hand_asset, i) for i in
range(self.num_shadow_hand_actuators)]
self.actuated_dof_indices = [self.gym.find_asset_dof_index(shadow_hand_asset, name) for name in
actuated_dof_names]
# set shadow_hand dof properties
shadow_hand_dof_props = self.gym.get_asset_dof_properties(shadow_hand_asset)
shadow_hand_another_dof_props = self.gym.get_asset_dof_properties(shadow_hand_another_asset)
self.shadow_hand_dof_lower_limits = []
self.shadow_hand_dof_upper_limits = []
self.shadow_hand_dof_default_pos = []
self.shadow_hand_dof_default_vel = []
self.sensors = []
sensor_pose = gymapi.Transform()
for i in range(self.num_shadow_hand_dofs):
self.shadow_hand_dof_lower_limits.append(shadow_hand_dof_props['lower'][i])
self.shadow_hand_dof_upper_limits.append(shadow_hand_dof_props['upper'][i])
self.shadow_hand_dof_default_pos.append(0.0)
self.shadow_hand_dof_default_vel.append(0.0)
self.actuated_dof_indices = to_torch(self.actuated_dof_indices, dtype=torch.long, device=self.device)
self.shadow_hand_dof_lower_limits = to_torch(self.shadow_hand_dof_lower_limits, device=self.device)
self.shadow_hand_dof_upper_limits = to_torch(self.shadow_hand_dof_upper_limits, device=self.device)
self.shadow_hand_dof_default_pos = to_torch(self.shadow_hand_dof_default_pos, device=self.device)
self.shadow_hand_dof_default_vel = to_torch(self.shadow_hand_dof_default_vel, device=self.device)
# load manipulated object and goal assets
object_asset_options = gymapi.AssetOptions()
object_asset_options.density = 1000
object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
object_asset_options.disable_gravity = True
goal_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
self.num_object_bodies = self.gym.get_asset_rigid_body_count(object_asset)
self.num_object_shapes = self.gym.get_asset_rigid_shape_count(object_asset)
# create table asset
table_dims = gymapi.Vec3(0.3, 0.3, 0.4)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
asset_options.flip_visual_attachments = False
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
table_asset = self.gym.create_box(self.sim, table_dims.x, table_dims.y, table_dims.z, asset_options)
shadow_hand_start_pose = gymapi.Transform()
shadow_hand_start_pose.p = gymapi.Vec3(0, 0.05, 0.45)
shadow_hand_start_pose.r = gymapi.Quat().from_euler_zyx(0, 0, 0)
shadow_another_hand_start_pose = gymapi.Transform()
shadow_another_hand_start_pose.p = gymapi.Vec3(0, -1.25, 0.45)
shadow_another_hand_start_pose.r = gymapi.Quat().from_euler_zyx(0, 0, 3.14159)
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
pose_dx, pose_dy, pose_dz = -0, -0.65, 0.
object_start_pose.p.x = shadow_hand_start_pose.p.x + pose_dx
object_start_pose.p.y = shadow_hand_start_pose.p.y + pose_dy
object_start_pose.p.z = shadow_hand_start_pose.p.z + pose_dz
if self.object_type == "pen":
object_start_pose.p.z = shadow_hand_start_pose.p.z + 0.02
self.goal_displacement = gymapi.Vec3(-0., 0.0, 0.4)
self.goal_displacement_tensor = to_torch(
[self.goal_displacement.x, self.goal_displacement.y, self.goal_displacement.z], device=self.device)
goal_start_pose = gymapi.Transform()
goal_start_pose.p = object_start_pose.p + self.goal_displacement
goal_start_pose.p.z -= 0.0
table_pose = gymapi.Transform()
table_pose.p = gymapi.Vec3(0.0, -0.6, 0.5 * table_dims.z)
table_pose.r = gymapi.Quat().from_euler_zyx(-0., 0, 0)
# compute aggregate size
max_agg_bodies = self.num_shadow_hand_bodies * 2 + 2 * self.num_object_bodies + 1
max_agg_shapes = self.num_shadow_hand_shapes * 2 + 2 * self.num_object_shapes + 1
self.shadow_hands = []
self.envs = []
self.object_init_state = []
self.hand_start_states = []
self.hand_indices = []
self.another_hand_indices = []
self.fingertip_indices = []
self.object_indices = []
self.goal_object_indices = []
self.table_indices = []
self.fingertip_handles = [self.gym.find_asset_rigid_body_index(shadow_hand_asset, name) for name in
self.fingertips]
self.fingertip_another_handles = [self.gym.find_asset_rigid_body_index(shadow_hand_another_asset, name) for name
in self.a_fingertips]
# create fingertip force sensors, if needed
sensor_pose = gymapi.Transform()
for ft_handle in self.fingertip_handles:
self.gym.create_asset_force_sensor(shadow_hand_asset, ft_handle, sensor_pose)
for ft_a_handle in self.fingertip_another_handles:
self.gym.create_asset_force_sensor(shadow_hand_another_asset, ft_a_handle, sensor_pose)
if self.obs_type in ["point_cloud"]:
self.cameras = []
self.camera_tensors = []
self.camera_view_matrixs = []
self.camera_proj_matrixs = []
self.camera_props = gymapi.CameraProperties()
self.camera_props.width = 256
self.camera_props.height = 256
self.camera_props.enable_tensors = True
self.env_origin = torch.zeros((self.num_envs, 3), device=self.device, dtype=torch.float)
self.pointCloudDownsampleNum = 768
self.camera_u = torch.arange(0, self.camera_props.width, device=self.device)
self.camera_v = torch.arange(0, self.camera_props.height, device=self.device)
self.camera_v2, self.camera_u2 = torch.meshgrid(self.camera_v, self.camera_u, indexing='ij')
if self.point_cloud_debug:
import open3d as o3d
from bidexhands.utils.o3dviewer import PointcloudVisualizer
self.pointCloudVisualizer = PointcloudVisualizer()
self.pointCloudVisualizerInitialized = False
self.o3d_pc = o3d.geometry.PointCloud()
else:
self.pointCloudVisualizer = None
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# add hand - collision filter = -1 to use asset collision filters set in mjcf loader
shadow_hand_actor = self.gym.create_actor(env_ptr, shadow_hand_asset, shadow_hand_start_pose, "hand", i, 0,
0)
shadow_hand_another_actor = self.gym.create_actor(env_ptr, shadow_hand_another_asset,
shadow_another_hand_start_pose, "another_hand", i, 0, 0)
self.hand_start_states.append(
[shadow_hand_start_pose.p.x, shadow_hand_start_pose.p.y, shadow_hand_start_pose.p.z,
shadow_hand_start_pose.r.x, shadow_hand_start_pose.r.y, shadow_hand_start_pose.r.z,
shadow_hand_start_pose.r.w,
0, 0, 0, 0, 0, 0])
self.gym.set_actor_dof_properties(env_ptr, shadow_hand_actor, shadow_hand_dof_props)
hand_idx = self.gym.get_actor_index(env_ptr, shadow_hand_actor, gymapi.DOMAIN_SIM)
self.hand_indices.append(hand_idx)
self.gym.set_actor_dof_properties(env_ptr, shadow_hand_another_actor, shadow_hand_another_dof_props)
another_hand_idx = self.gym.get_actor_index(env_ptr, shadow_hand_another_actor, gymapi.DOMAIN_SIM)
self.another_hand_indices.append(another_hand_idx)
# randomize colors and textures for rigid body
num_bodies = self.gym.get_actor_rigid_body_count(env_ptr, shadow_hand_actor)
hand_rigid_body_index = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19, 20],
[21, 22, 23, 24, 25]]
for n in self.agent_index[0]:
colorx = random.uniform(0, 1)
colory = random.uniform(0, 1)
colorz = random.uniform(0, 1)
for m in n:
for o in hand_rigid_body_index[m]:
self.gym.set_rigid_body_color(env_ptr, shadow_hand_actor, o, gymapi.MESH_VISUAL,
gymapi.Vec3(colorx, colory, colorz))
for n in self.agent_index[1]:
colorx = random.uniform(0, 1)
colory = random.uniform(0, 1)
colorz = random.uniform(0, 1)
for m in n:
for o in hand_rigid_body_index[m]:
self.gym.set_rigid_body_color(env_ptr, shadow_hand_another_actor, o, gymapi.MESH_VISUAL,
gymapi.Vec3(colorx, colory, colorz))
# gym.set_rigid_body_texture(env, actor_handles[-1], n, gymapi.MESH_VISUAL,
# loaded_texture_handle_list[random.randint(0, len(loaded_texture_handle_list)-1)])
# create fingertip force-torque sensors
self.gym.enable_actor_dof_force_sensors(env_ptr, shadow_hand_actor)
self.gym.enable_actor_dof_force_sensors(env_ptr, shadow_hand_another_actor)
# add object
object_handle = self.gym.create_actor(env_ptr, object_asset, object_start_pose, "object", i, 0, 0)
self.object_init_state.append([object_start_pose.p.x, object_start_pose.p.y, object_start_pose.p.z,
object_start_pose.r.x, object_start_pose.r.y, object_start_pose.r.z,
object_start_pose.r.w,
0, 0, 0, 0, 0, 0])
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
self.object_indices.append(object_idx)
# add goal object
goal_handle = self.gym.create_actor(env_ptr, goal_asset, goal_start_pose, "goal_object", i + self.num_envs,
0, 0)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
# add table
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, "table", i, -1, 0)
self.gym.set_rigid_body_texture(env_ptr, table_handle, 0, gymapi.MESH_VISUAL, table_texture_handle)
table_idx = self.gym.get_actor_index(env_ptr, table_handle, gymapi.DOMAIN_SIM)
self.table_indices.append(table_idx)
# set friction
table_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, table_handle)
object_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, object_handle)
table_shape_props[0].friction = 3
object_shape_props[0].friction = 3
self.gym.set_actor_rigid_shape_properties(env_ptr, table_handle, table_shape_props)
self.gym.set_actor_rigid_shape_properties(env_ptr, object_handle, object_shape_props)
if self.object_type != "block":
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
if self.obs_type in ["point_cloud"]:
camera_handle = self.gym.create_camera_sensor(env_ptr, self.camera_props)
self.gym.set_camera_location(camera_handle, env_ptr, gymapi.Vec3(0.25, -0.57, 0.75),
gymapi.Vec3(-0.24, -0.57, 0.25))
camera_tensor = self.gym.get_camera_image_gpu_tensor(self.sim, env_ptr, camera_handle,
gymapi.IMAGE_DEPTH)
torch_cam_tensor = gymtorch.wrap_tensor(camera_tensor)
cam_vinv = torch.inverse(
(torch.tensor(self.gym.get_camera_view_matrix(self.sim, env_ptr, camera_handle)))).to(self.device)
cam_proj = torch.tensor(self.gym.get_camera_proj_matrix(self.sim, env_ptr, camera_handle),
device=self.device)
origin = self.gym.get_env_origin(env_ptr)
self.env_origin[i][0] = origin.x
self.env_origin[i][1] = origin.y
self.env_origin[i][2] = origin.z
self.camera_tensors.append(torch_cam_tensor)
self.camera_view_matrixs.append(cam_vinv)
self.camera_proj_matrixs.append(cam_proj)
self.cameras.append(camera_handle)
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.shadow_hands.append(shadow_hand_actor)
self.object_init_state = to_torch(self.object_init_state, device=self.device, dtype=torch.float).view(
self.num_envs, 13)
self.goal_states = self.object_init_state.clone()
# self.goal_pose = self.goal_states[:, 0:7]
# self.goal_pos = self.goal_states[:, 0:3]
# self.goal_rot = self.goal_states[:, 3:7]
# self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.hand_start_states = to_torch(self.hand_start_states, device=self.device).view(self.num_envs, 13)
self.fingertip_handles = to_torch(self.fingertip_handles, dtype=torch.long, device=self.device)
self.fingertip_another_handles = to_torch(self.fingertip_another_handles, dtype=torch.long, device=self.device)
self.hand_indices = to_torch(self.hand_indices, dtype=torch.long, device=self.device)
self.another_hand_indices = to_torch(self.another_hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(self.object_indices, dtype=torch.long, device=self.device)
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
self.table_indices = to_torch(self.table_indices, dtype=torch.long, device=self.device)
def compute_reward(self, actions):
"""
Compute the reward of all environment. The core function is compute_hand_reward(
self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.successes, self.consecutive_successes,
self.max_episode_length, self.object_pos, self.object_rot, self.goal_pos, self.goal_rot, self.pot_left_handle_pos, self.pot_right_handle_pos,
self.left_hand_pos, self.right_hand_pos,
self.dist_reward_scale, self.rot_reward_scale, self.rot_eps, self.actions, self.action_penalty_scale,
self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty,
self.max_consecutive_successes, self.av_factor, (self.object_type == "pen")
)
, which we will introduce in detail there
Args:
actions (tensor): Actions of agents in the all environment
"""
self.rew_buf[:], self.reset_buf[:], self.reset_goal_buf[:], self.progress_buf[:], self.successes[
:], self.consecutive_successes[
:] = compute_hand_reward(
self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.successes,
self.consecutive_successes,
self.max_episode_length, self.object_pos, self.object_rot, self.goal_pos, self.goal_rot,
self.pot_left_handle_pos, self.pot_right_handle_pos,
self.left_hand_pos, self.right_hand_pos,
self.dist_reward_scale, self.rot_reward_scale, self.rot_eps, self.actions, self.action_penalty_scale,
self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty,
self.max_consecutive_successes, self.av_factor, (self.object_type == "pen")
)
self.extras['successes'] = self.successes
self.extras['consecutive_successes'] = self.consecutive_successes
if self.print_success_stat:
self.total_resets = self.total_resets + self.reset_buf.sum()
direct_average_successes = self.total_successes + self.successes.sum()
self.total_successes = self.total_successes + (self.successes * self.reset_buf).sum()
# The direct average shows the overall result more quickly, but slightly undershoots long term
# policy performance.
print("Direct average consecutive successes = {:.1f}".format(
direct_average_successes / (self.total_resets + self.num_envs)))
if self.total_resets > 0:
print("Post-Reset average consecutive successes = {:.1f}".format(
self.total_successes / self.total_resets))
def compute_observations(self):
"""
Compute the observations of all environment. The core function is self.compute_full_state(True),
which we will introduce in detail there
"""
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
if self.obs_type in ["point_cloud"]:
self.gym.render_all_camera_sensors(self.sim)
self.gym.start_access_image_tensors(self.sim)
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
self.pot_right_handle_pos = self.object_pos + quat_apply(self.object_rot,
to_torch([0, 1, 0], device=self.device).repeat(
self.num_envs, 1) * 0.15)
self.pot_right_handle_pos = self.pot_right_handle_pos + quat_apply(self.object_rot, to_torch([0, 0, 1],
device=self.device).repeat(
self.num_envs, 1) * 0.06)
self.pot_left_handle_pos = self.object_pos + quat_apply(self.object_rot,
to_torch([0, 1, 0], device=self.device).repeat(
self.num_envs, 1) * -0.15)
self.pot_left_handle_pos = self.pot_left_handle_pos + quat_apply(self.object_rot,
to_torch([0, 0, 1], device=self.device).repeat(
self.num_envs, 1) * 0.06)
self.left_hand_pos = self.rigid_body_states[:, 3 + 26, 0:3]
self.left_hand_rot = self.rigid_body_states[:, 3 + 26, 3:7]
self.left_hand_pos = self.left_hand_pos + quat_apply(self.left_hand_rot,
to_torch([0, 0, 1], device=self.device).repeat(
self.num_envs, 1) * 0.08)
self.left_hand_pos = self.left_hand_pos + quat_apply(self.left_hand_rot,
to_torch([0, 1, 0], device=self.device).repeat(
self.num_envs, 1) * -0.02)
self.right_hand_pos = self.rigid_body_states[:, 3, 0:3]
self.right_hand_rot = self.rigid_body_states[:, 3, 3:7]
self.right_hand_pos = self.right_hand_pos + quat_apply(self.right_hand_rot,
to_torch([0, 0, 1], device=self.device).repeat(
self.num_envs, 1) * 0.08)
self.right_hand_pos = self.right_hand_pos + quat_apply(self.right_hand_rot,
to_torch([0, 1, 0], device=self.device).repeat(
self.num_envs, 1) * -0.02)
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
self.fingertip_state = self.rigid_body_states[:, self.fingertip_handles][:, :, 0:13]
self.fingertip_pos = self.rigid_body_states[:, self.fingertip_handles][:, :, 0:3]
self.fingertip_another_state = self.rigid_body_states[:, self.fingertip_another_handles][:, :, 0:13]
self.fingertip_another_pos = self.rigid_body_states[:, self.fingertip_another_handles][:, :, 0:3]
if self.obs_type == "full_state":
self.compute_full_state()
elif self.obs_type == "point_cloud":
self.compute_point_cloud_observation()
if self.asymmetric_obs:
self.compute_full_state(True)
def compute_full_state(self, asymm_obs=False):
"""
Compute the observations of all environment. The observation is composed of three parts:
the state values of the left and right hands, and the information of objects and target.
The state values of the left and right hands were the same for each task, including hand
joint and finger positions, velocity, and force information. The detail 428-dimensional
observational space as shown in below:
Index Description
0 - 23 right shadow hand dof position
24 - 47 right shadow hand dof velocity
48 - 71 right shadow hand dof force
72 - 136 right shadow hand fingertip pose, linear velocity, angle velocity (5 x 13)
137 - 166 right shadow hand fingertip force, torque (5 x 6)
167 - 169 right shadow hand base position
170 - 172 right shadow hand base rotation
173 - 198 right shadow hand actions
199 - 222 left shadow hand dof position
223 - 246 left shadow hand dof velocity
247 - 270 left shadow hand dof force
271 - 335 left shadow hand fingertip pose, linear velocity, angle velocity (5 x 13)
336 - 365 left shadow hand fingertip force, torque (5 x 6)
366 - 368 left shadow hand base position
369 - 371 left shadow hand base rotation
372 - 397 left shadow hand actions
398 - 404 object pose
405 - 407 object linear velocity
408 - 410 object angle velocity
411 - 417 goal pose
418 - 421 goal rot - object rot
422 - 424 object right handle position
425 - 427 object left handle position
"""
num_ft_states = 13 * int(self.num_fingertips / 2) # 65
num_ft_force_torques = 6 * int(self.num_fingertips / 2) # 30
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits,
self.shadow_hand_dof_upper_limits)
self.obs_buf[:,
self.num_shadow_hand_dofs:2 * self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.obs_buf[:,
2 * self.num_shadow_hand_dofs:3 * self.num_shadow_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor[
:, :24]
fingertip_obs_start = 72 # 168 = 157 + 11
self.obs_buf[:, fingertip_obs_start:fingertip_obs_start + num_ft_states] = self.fingertip_state.reshape(
self.num_envs, num_ft_states)
self.obs_buf[:, fingertip_obs_start + num_ft_states:fingertip_obs_start + num_ft_states +
num_ft_force_torques] = self.force_torque_obs_scale * self.vec_sensor_tensor[
:,
:30]
hand_pose_start = fingertip_obs_start + 95
self.obs_buf[:, hand_pose_start:hand_pose_start + 3] = self.right_hand_pos
self.obs_buf[:, hand_pose_start + 3:hand_pose_start + 4] = \
get_euler_xyz(self.hand_orientations[self.hand_indices, :])[0].unsqueeze(-1)
self.obs_buf[:, hand_pose_start + 4:hand_pose_start + 5] = \
get_euler_xyz(self.hand_orientations[self.hand_indices, :])[1].unsqueeze(-1)
self.obs_buf[:, hand_pose_start + 5:hand_pose_start + 6] = \
get_euler_xyz(self.hand_orientations[self.hand_indices, :])[2].unsqueeze(-1)
action_obs_start = hand_pose_start + 6
self.obs_buf[:, action_obs_start:action_obs_start + 26] = self.actions[:, :26]
# another_hand
another_hand_start = action_obs_start + 26
self.obs_buf[:, another_hand_start:self.num_shadow_hand_dofs + another_hand_start] = unscale(
self.shadow_hand_another_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:,
self.num_shadow_hand_dofs + another_hand_start:2 * self.num_shadow_hand_dofs + another_hand_start] = self.vel_obs_scale * self.shadow_hand_another_dof_vel
self.obs_buf[:,
2 * self.num_shadow_hand_dofs + another_hand_start:3 * self.num_shadow_hand_dofs + another_hand_start] = self.force_torque_obs_scale * self.dof_force_tensor[
:,
24:48]
fingertip_another_obs_start = another_hand_start + 72
self.obs_buf[:,
fingertip_another_obs_start:fingertip_another_obs_start + num_ft_states] = self.fingertip_another_state.reshape(
self.num_envs, num_ft_states)
self.obs_buf[:, fingertip_another_obs_start + num_ft_states:fingertip_another_obs_start + num_ft_states +
num_ft_force_torques] = self.force_torque_obs_scale * self.vec_sensor_tensor[
:,
30:]
hand_another_pose_start = fingertip_another_obs_start + 95
self.obs_buf[:, hand_another_pose_start:hand_another_pose_start + 3] = self.left_hand_pos
self.obs_buf[:, hand_another_pose_start + 3:hand_another_pose_start + 4] = \
get_euler_xyz(self.hand_orientations[self.another_hand_indices, :])[0].unsqueeze(-1)
self.obs_buf[:, hand_another_pose_start + 4:hand_another_pose_start + 5] = \
get_euler_xyz(self.hand_orientations[self.another_hand_indices, :])[1].unsqueeze(-1)
self.obs_buf[:, hand_another_pose_start + 5:hand_another_pose_start + 6] = \
get_euler_xyz(self.hand_orientations[self.another_hand_indices, :])[2].unsqueeze(-1)
action_another_obs_start = hand_another_pose_start + 6
self.obs_buf[:, action_another_obs_start:action_another_obs_start + 26] = self.actions[:, 26:]
obj_obs_start = action_another_obs_start + 26 # 144
self.obs_buf[:, obj_obs_start:obj_obs_start + 7] = self.object_pose
self.obs_buf[:, obj_obs_start + 7:obj_obs_start + 10] = self.object_linvel
self.obs_buf[:, obj_obs_start + 10:obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, obj_obs_start + 13:obj_obs_start + 16] = self.pot_right_handle_pos
self.obs_buf[:, obj_obs_start + 16:obj_obs_start + 19] = self.pot_left_handle_pos
# goal_obs_start = obj_obs_start + 13 # 157 = 144 + 13
# self.obs_buf[:, goal_obs_start:goal_obs_start + 7] = self.goal_pose
# self.obs_buf[:, goal_obs_start + 7:goal_obs_start + 11] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
def compute_point_cloud_observation(self, collect_demonstration=False):
"""
Compute the observations of all environment. The observation is composed of three parts:
the state values of the left and right hands, and the information of objects and target.
The state values of the left and right hands were the same for each task, including hand
joint and finger positions, velocity, and force information. The detail 428-dimensional
observational space as shown in below:
Index Description
0 - 23 right shadow hand dof position
24 - 47 right shadow hand dof velocity
48 - 71 right shadow hand dof force
72 - 136 right shadow hand fingertip pose, linear velocity, angle velocity (5 x 13)
137 - 166 right shadow hand fingertip force, torque (5 x 6)
167 - 169 right shadow hand base position
170 - 172 right shadow hand base rotation
173 - 198 right shadow hand actions
199 - 222 left shadow hand dof position
223 - 246 left shadow hand dof velocity
247 - 270 left shadow hand dof force
271 - 335 left shadow hand fingertip pose, linear velocity, angle velocity (5 x 13)
336 - 365 left shadow hand fingertip force, torque (5 x 6)
366 - 368 left shadow hand base position
369 - 371 left shadow hand base rotation
372 - 397 left shadow hand actions
398 - 404 object pose
405 - 407 object linear velocity
408 - 410 object angle velocity
411 - 417 goal pose
418 - 421 goal rot - object rot
422 - 424 object right handle position
425 - 427 object left handle position
"""
num_ft_states = 13 * int(self.num_fingertips / 2) # 65
num_ft_force_torques = 6 * int(self.num_fingertips / 2) # 30
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits,
self.shadow_hand_dof_upper_limits)
self.obs_buf[:,
self.num_shadow_hand_dofs:2 * self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.obs_buf[:,
2 * self.num_shadow_hand_dofs:3 * self.num_shadow_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor[
:, :24]
fingertip_obs_start = 72 # 168 = 157 + 11
self.obs_buf[:, fingertip_obs_start:fingertip_obs_start + num_ft_states] = self.fingertip_state.reshape(
self.num_envs, num_ft_states)
self.obs_buf[:, fingertip_obs_start + num_ft_states:fingertip_obs_start + num_ft_states +
num_ft_force_torques] = self.force_torque_obs_scale * self.vec_sensor_tensor[
:,
:30]
hand_pose_start = fingertip_obs_start + 95
self.obs_buf[:, hand_pose_start:hand_pose_start + 3] = self.right_hand_pos
self.obs_buf[:, hand_pose_start + 3:hand_pose_start + 4] = \
get_euler_xyz(self.hand_orientations[self.hand_indices, :])[0].unsqueeze(-1)
self.obs_buf[:, hand_pose_start + 4:hand_pose_start + 5] = \
get_euler_xyz(self.hand_orientations[self.hand_indices, :])[1].unsqueeze(-1)
self.obs_buf[:, hand_pose_start + 5:hand_pose_start + 6] = \
get_euler_xyz(self.hand_orientations[self.hand_indices, :])[2].unsqueeze(-1)
action_obs_start = hand_pose_start + 6
self.obs_buf[:, action_obs_start:action_obs_start + 26] = self.actions[:, :26]
# another_hand
another_hand_start = action_obs_start + 26
self.obs_buf[:, another_hand_start:self.num_shadow_hand_dofs + another_hand_start] = unscale(
self.shadow_hand_another_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:,
self.num_shadow_hand_dofs + another_hand_start:2 * self.num_shadow_hand_dofs + another_hand_start] = self.vel_obs_scale * self.shadow_hand_another_dof_vel
self.obs_buf[:,
2 * self.num_shadow_hand_dofs + another_hand_start:3 * self.num_shadow_hand_dofs + another_hand_start] = self.force_torque_obs_scale * self.dof_force_tensor[
:,
24:48]
fingertip_another_obs_start = another_hand_start + 72
self.obs_buf[:,
fingertip_another_obs_start:fingertip_another_obs_start + num_ft_states] = self.fingertip_another_state.reshape(
self.num_envs, num_ft_states)
self.obs_buf[:, fingertip_another_obs_start + num_ft_states:fingertip_another_obs_start + num_ft_states +
num_ft_force_torques] = self.force_torque_obs_scale * self.vec_sensor_tensor[
:,
30:]
hand_another_pose_start = fingertip_another_obs_start + 95
self.obs_buf[:, hand_another_pose_start:hand_another_pose_start + 3] = self.left_hand_pos
self.obs_buf[:, hand_another_pose_start + 3:hand_another_pose_start + 4] = \
get_euler_xyz(self.hand_orientations[self.another_hand_indices, :])[0].unsqueeze(-1)
self.obs_buf[:, hand_another_pose_start + 4:hand_another_pose_start + 5] = \
get_euler_xyz(self.hand_orientations[self.another_hand_indices, :])[1].unsqueeze(-1)
self.obs_buf[:, hand_another_pose_start + 5:hand_another_pose_start + 6] = \
get_euler_xyz(self.hand_orientations[self.another_hand_indices, :])[2].unsqueeze(-1)
action_another_obs_start = hand_another_pose_start + 6
self.obs_buf[:, action_another_obs_start:action_another_obs_start + 26] = self.actions[:, 26:]
obj_obs_start = action_another_obs_start + 26 # 144
self.obs_buf[:, obj_obs_start:obj_obs_start + 7] = self.object_pose
self.obs_buf[:, obj_obs_start + 7:obj_obs_start + 10] = self.object_linvel
self.obs_buf[:, obj_obs_start + 10:obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, obj_obs_start + 13:obj_obs_start + 16] = self.pot_right_handle_pos
self.obs_buf[:, obj_obs_start + 16:obj_obs_start + 19] = self.pot_left_handle_pos
# goal_obs_start = obj_obs_start + 13 # 157 = 144 + 13
# self.obs_buf[:, goal_obs_start:goal_obs_start + 7] = self.goal_pose
# self.obs_buf[:, goal_obs_start + 7:goal_obs_start + 11] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
point_clouds = torch.zeros((self.num_envs, self.pointCloudDownsampleNum, 3), device=self.device)
if self.camera_debug:
import matplotlib.pyplot as plt
self.camera_rgba_debug_fig = plt.figure("CAMERA_RGBD_DEBUG")
camera_rgba_image = self.camera_visulization(is_depth_image=False)
plt.imshow(camera_rgba_image)
plt.pause(1e-9)
for i in range(self.num_envs):
# Here is an example. In practice, it's better not to convert tensor from GPU to CPU
points = depth_image_to_point_cloud_GPU(self.camera_tensors[i], self.camera_view_matrixs[i],
self.camera_proj_matrixs[i], self.camera_u2, self.camera_v2,
self.camera_props.width, self.camera_props.height, 10, self.device)
if points.shape[0] > 0:
selected_points = self.sample_points(points, sample_num=self.pointCloudDownsampleNum,
sample_mathed='random')
else:
selected_points = torch.zeros((self.num_envs, self.pointCloudDownsampleNum, 3), device=self.device)
point_clouds[i] = selected_points
if self.pointCloudVisualizer != None:
import open3d as o3d
points = point_clouds[0, :, :3].cpu().numpy()
# colors = plt.get_cmap()(point_clouds[0, :, 3].cpu().numpy())
self.o3d_pc.points = o3d.utility.Vector3dVector(points)
# self.o3d_pc.colors = o3d.utility.Vector3dVector(colors[..., :3])
if self.pointCloudVisualizerInitialized == False:
self.pointCloudVisualizer.add_geometry(self.o3d_pc)
self.pointCloudVisualizerInitialized = True
else:
self.pointCloudVisualizer.update(self.o3d_pc)
self.gym.end_access_image_tensors(self.sim)
point_clouds -= self.env_origin.view(self.num_envs, 1, 3)
point_clouds_start = obj_obs_start + 19
self.obs_buf[:, point_clouds_start:].copy_(point_clouds.view(self.num_envs, self.pointCloudDownsampleNum * 3))
def reset_target_pose(self, env_ids, apply_reset=False):
"""
Reset and randomize the goal pose
Args:
env_ids (tensor): The index of the environment that needs to reset goal pose
apply_reset (bool): Whether to reset the goal directly here, usually used
when the same task wants to complete multiple goals
"""
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 4), device=self.device)
new_rot = randomize_rotation(rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids],
self.y_unit_tensor[env_ids])
self.goal_states[env_ids, 0:3] = self.goal_init_state[env_ids, 0:3]
# self.goal_states[env_ids, 1] -= 0.25
self.goal_states[env_ids, 2] += 0.4
# self.goal_states[env_ids, 3:7] = new_rot
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids,
0:3] + self.goal_displacement_tensor
self.root_state_tensor[self.goal_object_indices[env_ids], 3:7] = self.goal_states[env_ids, 3:7]
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13] = torch.zeros_like(
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13])
if apply_reset:
goal_object_indices = self.goal_object_indices[env_ids].to(torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(goal_object_indices), len(env_ids))
self.reset_goal_buf[env_ids] = 0