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franka_cabinet.py
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# Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from isaacgym.torch_utils import *
from .base.vec_task import VecTask
class FrankaCabinet(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.action_scale = self.cfg["env"]["actionScale"]
self.start_position_noise = self.cfg["env"]["startPositionNoise"]
self.start_rotation_noise = self.cfg["env"]["startRotationNoise"]
self.num_props = self.cfg["env"]["numProps"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dof_vel_scale = self.cfg["env"]["dofVelocityScale"]
self.dist_reward_scale = self.cfg["env"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rotRewardScale"]
self.around_handle_reward_scale = self.cfg["env"]["aroundHandleRewardScale"]
self.open_reward_scale = self.cfg["env"]["openRewardScale"]
self.finger_dist_reward_scale = self.cfg["env"]["fingerDistRewardScale"]
self.action_penalty_scale = self.cfg["env"]["actionPenaltyScale"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.up_axis = "z"
self.up_axis_idx = 2
self.distX_offset = 0.04
self.dt = 1/60.
# prop dimensions
self.prop_width = 0.08
self.prop_height = 0.08
self.prop_length = 0.08
self.prop_spacing = 0.09
num_obs = 23
num_acts = 9
self.cfg["env"]["numObservations"] = 23
self.cfg["env"]["numActions"] = 9
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id,
headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# 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)
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.franka_default_dof_pos = to_torch(
[1.157, -1.066, -0.155, -2.239, -1.841, 1.003, 0.469, 0.035, 0.035], device=self.device)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.franka_dof_state = self.dof_state.view(
self.num_envs, -1, 2)[:, :self.num_franka_dofs]
self.franka_dof_pos = self.franka_dof_state[..., 0]
self.franka_dof_vel = self.franka_dof_state[..., 1]
self.cabinet_dof_state = self.dof_state.view(
self.num_envs, -1, 2)[:, self.num_franka_dofs:]
self.cabinet_dof_pos = self.cabinet_dof_state[..., 0]
self.cabinet_dof_vel = self.cabinet_dof_state[..., 1]
print('debug')
print(self.num_franka_dofs)
print(self.dof_state.shape)
print(self.cabinet_dof_pos.shape)
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(self.num_envs, -1, 13)
if self.num_props > 0:
self.prop_states = self.root_state_tensor[:, 2:]
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
self.franka_dof_targets = torch.zeros(
(self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(
self.num_envs * (2 + self.num_props), dtype=torch.int32, device=self.device).view(self.num_envs, -1)
self.reset_idx(torch.arange(self.num_envs, device=self.device))
def create_sim(self):
self.sim_params.up_axis = gymapi.UP_AXIS_Z
self.sim_params.gravity.x = 0
self.sim_params.gravity.y = 0
self.sim_params.gravity.z = -9.81
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):
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):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(
os.path.abspath(__file__)), "../../assets")
franka_asset_file = "urdf/franka_description/robots/franka_panda.urdf"
cabinet_asset_file = "urdf/sektion_cabinet_model/urdf/sektion_cabinet_2.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(
__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
franka_asset_file = self.cfg["env"]["asset"].get(
"assetFileNameFranka", franka_asset_file)
cabinet_asset_file = self.cfg["env"]["asset"].get(
"assetFileNameCabinet", cabinet_asset_file)
# load franka asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = True
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
asset_options.use_mesh_materials = True
franka_asset = self.gym.load_asset(
self.sim, asset_root, franka_asset_file, asset_options)
# load cabinet asset
asset_options.flip_visual_attachments = False
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = False
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
asset_options.armature = 0.005
cabinet_asset = self.gym.load_asset(
self.sim, asset_root, cabinet_asset_file, asset_options)
franka_dof_stiffness = to_torch(
[400, 400, 400, 400, 400, 400, 400, 1.0e6, 1.0e6], dtype=torch.float, device=self.device)
franka_dof_damping = to_torch(
[80, 80, 80, 80, 80, 80, 80, 1.0e2, 1.0e2], dtype=torch.float, device=self.device)
self.num_franka_bodies = self.gym.get_asset_rigid_body_count(
franka_asset)
self.num_franka_dofs = self.gym.get_asset_dof_count(franka_asset)
self.num_cabinet_bodies = self.gym.get_asset_rigid_body_count(
cabinet_asset)
self.num_cabinet_dofs = self.gym.get_asset_dof_count(cabinet_asset)
print("num franka bodies: ", self.num_franka_bodies)
print("num franka dofs: ", self.num_franka_dofs)
print("num cabinet bodies: ", self.num_cabinet_bodies)
print("num cabinet dofs: ", self.num_cabinet_dofs)
# set franka dof properties
franka_dof_props = self.gym.get_asset_dof_properties(franka_asset)
self.franka_dof_lower_limits = []
self.franka_dof_upper_limits = []
for i in range(self.num_franka_dofs):
franka_dof_props['driveMode'][i] = gymapi.DOF_MODE_POS
if self.physics_engine == gymapi.SIM_PHYSX:
franka_dof_props['stiffness'][i] = franka_dof_stiffness[i]
franka_dof_props['damping'][i] = franka_dof_damping[i]
else:
franka_dof_props['stiffness'][i] = 7000.0
franka_dof_props['damping'][i] = 50.0
self.franka_dof_lower_limits.append(franka_dof_props['lower'][i])
self.franka_dof_upper_limits.append(franka_dof_props['upper'][i])
self.franka_dof_lower_limits = to_torch(
self.franka_dof_lower_limits, device=self.device)
self.franka_dof_upper_limits = to_torch(
self.franka_dof_upper_limits, device=self.device)
self.franka_dof_speed_scales = torch.ones_like(
self.franka_dof_lower_limits)
self.franka_dof_speed_scales[[7, 8]] = 0.1
franka_dof_props['effort'][7] = 200
franka_dof_props['effort'][8] = 200
# set cabinet dof properties
cabinet_dof_props = self.gym.get_asset_dof_properties(cabinet_asset)
for i in range(self.num_cabinet_dofs):
cabinet_dof_props['damping'][i] = 10.0
# create prop assets
box_opts = gymapi.AssetOptions()
box_opts.density = 400
prop_asset = self.gym.create_box(
self.sim, self.prop_width, self.prop_height, self.prop_width, box_opts)
franka_start_pose = gymapi.Transform()
franka_start_pose.p = gymapi.Vec3(1.0, 0.0, 0.0)
franka_start_pose.r = gymapi.Quat(0.0, 0.0, 1.0, 0.0)
cabinet_start_pose = gymapi.Transform()
cabinet_start_pose.p = gymapi.Vec3(
*get_axis_params(0.4, self.up_axis_idx))
# compute aggregate size
num_franka_bodies = self.gym.get_asset_rigid_body_count(franka_asset)
num_franka_shapes = self.gym.get_asset_rigid_shape_count(franka_asset)
num_cabinet_bodies = self.gym.get_asset_rigid_body_count(cabinet_asset)
num_cabinet_shapes = self.gym.get_asset_rigid_shape_count(
cabinet_asset)
num_prop_bodies = self.gym.get_asset_rigid_body_count(prop_asset)
num_prop_shapes = self.gym.get_asset_rigid_shape_count(prop_asset)
max_agg_bodies = num_franka_bodies + \
num_cabinet_bodies + self.num_props * num_prop_bodies
max_agg_shapes = num_franka_shapes + \
num_cabinet_shapes + self.num_props * num_prop_shapes
self.frankas = []
self.cabinets = []
self.default_prop_states = []
self.prop_start = []
self.envs = []
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 >= 3:
self.gym.begin_aggregate(
env_ptr, max_agg_bodies, max_agg_shapes, True)
franka_actor = self.gym.create_actor(
env_ptr, franka_asset, franka_start_pose, "franka", i, 1, 0)
self.gym.set_actor_dof_properties(
env_ptr, franka_actor, franka_dof_props)
if self.aggregate_mode == 2:
self.gym.begin_aggregate(
env_ptr, max_agg_bodies, max_agg_shapes, True)
cabinet_pose = cabinet_start_pose
cabinet_pose.p.x += self.start_position_noise * \
(np.random.rand() - 0.5)
dz = 0.5 * np.random.rand()
dy = np.random.rand() - 0.5
cabinet_pose.p.y += self.start_position_noise * dy
cabinet_pose.p.z += self.start_position_noise * dz
cabinet_actor = self.gym.create_actor(
env_ptr, cabinet_asset, cabinet_pose, "cabinet", i, 2, 0)
self.gym.set_actor_dof_properties(
env_ptr, cabinet_actor, cabinet_dof_props)
if self.aggregate_mode == 1:
self.gym.begin_aggregate(
env_ptr, max_agg_bodies, max_agg_shapes, True)
if self.num_props > 0:
self.prop_start.append(self.gym.get_sim_actor_count(self.sim))
drawer_handle = self.gym.find_actor_rigid_body_handle(
env_ptr, cabinet_actor, "drawer_top")
drawer_pose = self.gym.get_rigid_transform(
env_ptr, drawer_handle)
props_per_row = int(np.ceil(np.sqrt(self.num_props)))
xmin = -0.5 * self.prop_spacing * (props_per_row - 1)
yzmin = -0.5 * self.prop_spacing * (props_per_row - 1)
prop_count = 0
for j in range(props_per_row):
prop_up = yzmin + j * self.prop_spacing
for k in range(props_per_row):
if prop_count >= self.num_props:
break
propx = xmin + k * self.prop_spacing
prop_state_pose = gymapi.Transform()
prop_state_pose.p.x = drawer_pose.p.x + propx
propz, propy = 0, prop_up
prop_state_pose.p.y = drawer_pose.p.y + propy
prop_state_pose.p.z = drawer_pose.p.z + propz
prop_state_pose.r = gymapi.Quat(0, 0, 0, 1)
prop_handle = self.gym.create_actor(
env_ptr, prop_asset, prop_state_pose, "prop{}".format(prop_count), i, 0, 0)
prop_count += 1
prop_idx = j * props_per_row + k
self.default_prop_states.append([prop_state_pose.p.x, prop_state_pose.p.y, prop_state_pose.p.z,
prop_state_pose.r.x, prop_state_pose.r.y, prop_state_pose.r.z, prop_state_pose.r.w,
0, 0, 0, 0, 0, 0])
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.frankas.append(franka_actor)
self.cabinets.append(cabinet_actor)
self.hand_handle = self.gym.find_actor_rigid_body_handle(
env_ptr, franka_actor, "panda_link7")
self.drawer_handle = self.gym.find_actor_rigid_body_handle(
env_ptr, cabinet_actor, "drawer_top")
self.lfinger_handle = self.gym.find_actor_rigid_body_handle(
env_ptr, franka_actor, "panda_leftfinger")
self.rfinger_handle = self.gym.find_actor_rigid_body_handle(
env_ptr, franka_actor, "panda_rightfinger")
self.default_prop_states = to_torch(
self.default_prop_states, device=self.device, dtype=torch.float).view(self.num_envs, self.num_props, 13)
self.init_data()
def init_data(self):
hand = self.gym.find_actor_rigid_body_handle(
self.envs[0], self.frankas[0], "panda_link7")
lfinger = self.gym.find_actor_rigid_body_handle(
self.envs[0], self.frankas[0], "panda_leftfinger")
rfinger = self.gym.find_actor_rigid_body_handle(
self.envs[0], self.frankas[0], "panda_rightfinger")
hand_pose = self.gym.get_rigid_transform(self.envs[0], hand)
lfinger_pose = self.gym.get_rigid_transform(self.envs[0], lfinger)
rfinger_pose = self.gym.get_rigid_transform(self.envs[0], rfinger)
finger_pose = gymapi.Transform()
finger_pose.p = (lfinger_pose.p + rfinger_pose.p) * 0.5
finger_pose.r = lfinger_pose.r
hand_pose_inv = hand_pose.inverse()
grasp_pose_axis = 1
franka_local_grasp_pose = hand_pose_inv * finger_pose
franka_local_grasp_pose.p += gymapi.Vec3(
*get_axis_params(0.04, grasp_pose_axis))
self.franka_local_grasp_pos = to_torch([franka_local_grasp_pose.p.x, franka_local_grasp_pose.p.y,
franka_local_grasp_pose.p.z], device=self.device).repeat((self.num_envs, 1))
self.franka_local_grasp_rot = to_torch([franka_local_grasp_pose.r.x, franka_local_grasp_pose.r.y,
franka_local_grasp_pose.r.z, franka_local_grasp_pose.r.w], device=self.device).repeat((self.num_envs, 1))
drawer_local_grasp_pose = gymapi.Transform()
drawer_local_grasp_pose.p = gymapi.Vec3(
*get_axis_params(0.01, grasp_pose_axis, 0.3))
drawer_local_grasp_pose.r = gymapi.Quat(0, 0, 0, 1)
self.drawer_local_grasp_pos = to_torch([drawer_local_grasp_pose.p.x, drawer_local_grasp_pose.p.y,
drawer_local_grasp_pose.p.z], device=self.device).repeat((self.num_envs, 1))
self.drawer_local_grasp_rot = to_torch([drawer_local_grasp_pose.r.x, drawer_local_grasp_pose.r.y,
drawer_local_grasp_pose.r.z, drawer_local_grasp_pose.r.w], device=self.device).repeat((self.num_envs, 1))
self.gripper_forward_axis = to_torch(
[0, 0, 1], device=self.device).repeat((self.num_envs, 1))
self.drawer_inward_axis = to_torch(
[-1, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.gripper_up_axis = to_torch(
[0, 1, 0], device=self.device).repeat((self.num_envs, 1))
self.drawer_up_axis = to_torch(
[0, 0, 1], device=self.device).repeat((self.num_envs, 1))
self.franka_grasp_pos = torch.zeros_like(self.franka_local_grasp_pos)
self.franka_grasp_rot = torch.zeros_like(self.franka_local_grasp_rot)
self.franka_grasp_rot[..., -1] = 1 # xyzw
self.drawer_grasp_pos = torch.zeros_like(self.drawer_local_grasp_pos)
self.drawer_grasp_rot = torch.zeros_like(self.drawer_local_grasp_rot)
self.drawer_grasp_rot[..., -1] = 1
self.franka_lfinger_pos = torch.zeros_like(self.franka_local_grasp_pos)
self.franka_rfinger_pos = torch.zeros_like(self.franka_local_grasp_pos)
self.franka_lfinger_rot = torch.zeros_like(self.franka_local_grasp_rot)
self.franka_rfinger_rot = torch.zeros_like(self.franka_local_grasp_rot)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:] = compute_franka_reward(
self.reset_buf, self.progress_buf, self.actions, self.cabinet_dof_pos,
self.franka_grasp_pos, self.drawer_grasp_pos, self.franka_grasp_rot, self.drawer_grasp_rot,
self.franka_lfinger_pos, self.franka_rfinger_pos,
self.gripper_forward_axis, self.drawer_inward_axis, self.gripper_up_axis, self.drawer_up_axis,
self.num_envs, self.dist_reward_scale, self.rot_reward_scale, self.around_handle_reward_scale, self.open_reward_scale,
self.finger_dist_reward_scale, self.action_penalty_scale, self.distX_offset, self.max_episode_length
)
def compute_observations(self):
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)
hand_pos = self.rigid_body_states[:, self.hand_handle][:, 0:3]
hand_rot = self.rigid_body_states[:, self.hand_handle][:, 3:7]
drawer_pos = self.rigid_body_states[:, self.drawer_handle][:, 0:3]
drawer_rot = self.rigid_body_states[:, self.drawer_handle][:, 3:7]
self.franka_grasp_rot[:], self.franka_grasp_pos[:], self.drawer_grasp_rot[:], self.drawer_grasp_pos[:] = \
compute_grasp_transforms(hand_rot, hand_pos, self.franka_local_grasp_rot, self.franka_local_grasp_pos,
drawer_rot, drawer_pos, self.drawer_local_grasp_rot, self.drawer_local_grasp_pos
)
self.franka_lfinger_pos = self.rigid_body_states[:,
self.lfinger_handle][:, 0:3]
self.franka_rfinger_pos = self.rigid_body_states[:,
self.rfinger_handle][:, 0:3]
self.franka_lfinger_rot = self.rigid_body_states[:,
self.lfinger_handle][:, 3:7]
self.franka_rfinger_rot = self.rigid_body_states[:,
self.rfinger_handle][:, 3:7]
dof_pos_scaled = (2.0 * (self.franka_dof_pos - self.franka_dof_lower_limits)
/ (self.franka_dof_upper_limits - self.franka_dof_lower_limits) - 1.0)
to_target = self.drawer_grasp_pos - self.franka_grasp_pos
self.obs_buf = torch.cat((dof_pos_scaled, self.franka_dof_vel * self.dof_vel_scale, to_target,
self.cabinet_dof_pos[:, 3].unsqueeze(-1), self.cabinet_dof_vel[:, 3].unsqueeze(-1)), dim=-1)
return self.obs_buf
def reset_idx(self, env_ids):
env_ids_int32 = env_ids.to(dtype=torch.int32)
# reset franka
pos = tensor_clamp(
self.franka_default_dof_pos.unsqueeze(
0) + 0.25 * (torch.rand((len(env_ids), self.num_franka_dofs), device=self.device) - 0.5),
self.franka_dof_lower_limits, self.franka_dof_upper_limits)
self.franka_dof_pos[env_ids, :] = pos
self.franka_dof_vel[env_ids, :] = torch.zeros_like(
self.franka_dof_vel[env_ids])
self.franka_dof_targets[env_ids, :self.num_franka_dofs] = pos
# reset cabinet
self.cabinet_dof_state[env_ids, :] = torch.zeros_like(
self.cabinet_dof_state[env_ids])
# reset props
if self.num_props > 0:
prop_indices = self.global_indices[env_ids, 2:].flatten()
self.prop_states[env_ids] = self.default_prop_states[env_ids]
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(
self.root_state_tensor),
gymtorch.unwrap_tensor(prop_indices), len(prop_indices))
multi_env_ids_int32 = self.global_indices[env_ids, :2].flatten()
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(
self.franka_dof_targets),
gymtorch.unwrap_tensor(multi_env_ids_int32), len(multi_env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(
self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32), len(multi_env_ids_int32))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
targets = self.franka_dof_targets[:, :self.num_franka_dofs] + \
self.franka_dof_speed_scales * self.dt * self.actions * self.action_scale
self.franka_dof_targets[:, :self.num_franka_dofs] = tensor_clamp(
targets, self.franka_dof_lower_limits, self.franka_dof_upper_limits)
env_ids_int32 = torch.arange(
self.num_envs, dtype=torch.int32, device=self.device)
self.gym.set_dof_position_target_tensor(self.sim,
gymtorch.unwrap_tensor(self.franka_dof_targets))
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward(self.actions)
# debug viz
if self.viewer and self.debug_viz:
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
for i in range(self.num_envs):
px = (self.franka_grasp_pos[i] + quat_apply(self.franka_grasp_rot[i], to_torch(
[1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.franka_grasp_pos[i] + quat_apply(self.franka_grasp_rot[i], to_torch(
[0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.franka_grasp_pos[i] + quat_apply(self.franka_grasp_rot[i], to_torch(
[0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.franka_grasp_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], px[0], px[1], px[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0.1, 0.1, 0.85])
px = (self.drawer_grasp_pos[i] + quat_apply(self.drawer_grasp_rot[i], to_torch(
[1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.drawer_grasp_pos[i] + quat_apply(self.drawer_grasp_rot[i], to_torch(
[0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.drawer_grasp_pos[i] + quat_apply(self.drawer_grasp_rot[i], to_torch(
[0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.drawer_grasp_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], px[0], px[1], px[2]], [1, 0, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0, 1, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0, 0, 1])
px = (self.franka_lfinger_pos[i] + quat_apply(self.franka_lfinger_rot[i], to_torch(
[1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.franka_lfinger_pos[i] + quat_apply(self.franka_lfinger_rot[i], to_torch(
[0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.franka_lfinger_pos[i] + quat_apply(self.franka_lfinger_rot[i], to_torch(
[0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.franka_lfinger_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], px[0], px[1], px[2]], [1, 0, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0, 1, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0, 0, 1])
px = (self.franka_rfinger_pos[i] + quat_apply(self.franka_rfinger_rot[i], to_torch(
[1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.franka_rfinger_pos[i] + quat_apply(self.franka_rfinger_rot[i], to_torch(
[0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.franka_rfinger_pos[i] + quat_apply(self.franka_rfinger_rot[i], to_torch(
[0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.franka_rfinger_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], px[0], px[1], px[2]], [1, 0, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0, 1, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [
p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0, 0, 1])
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_franka_reward(
reset_buf, progress_buf, actions, cabinet_dof_pos,
franka_grasp_pos, drawer_grasp_pos, franka_grasp_rot, drawer_grasp_rot,
franka_lfinger_pos, franka_rfinger_pos,
gripper_forward_axis, drawer_inward_axis, gripper_up_axis, drawer_up_axis,
num_envs, dist_reward_scale, rot_reward_scale, around_handle_reward_scale, open_reward_scale,
finger_dist_reward_scale, action_penalty_scale, distX_offset, max_episode_length
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, int, float, float, float, float, float, float, float, float) -> Tuple[Tensor, Tensor]
# distance from hand to the drawer
d = torch.norm(franka_grasp_pos - drawer_grasp_pos, p=2, dim=-1)
dist_reward = 1.0 / (1.0 + d ** 2)
dist_reward *= dist_reward
dist_reward = torch.where(d <= 0.02, dist_reward * 2, dist_reward)
axis1 = tf_vector(franka_grasp_rot, gripper_forward_axis)
axis2 = tf_vector(drawer_grasp_rot, drawer_inward_axis)
axis3 = tf_vector(franka_grasp_rot, gripper_up_axis)
axis4 = tf_vector(drawer_grasp_rot, drawer_up_axis)
dot1 = torch.bmm(axis1.view(num_envs, 1, 3), axis2.view(
num_envs, 3, 1)).squeeze(-1).squeeze(-1) # alignment of forward axis for gripper
dot2 = torch.bmm(axis3.view(num_envs, 1, 3), axis4.view(
num_envs, 3, 1)).squeeze(-1).squeeze(-1) # alignment of up axis for gripper
# reward for matching the orientation of the hand to the drawer (fingers wrapped)
rot_reward = 0.5 * (torch.sign(dot1) * dot1 ** 2 +
torch.sign(dot2) * dot2 ** 2)
# bonus if left finger is above the drawer handle and right below
around_handle_reward = torch.zeros_like(rot_reward)
around_handle_reward = torch.where(franka_lfinger_pos[:, 2] > drawer_grasp_pos[:, 2],
torch.where(franka_rfinger_pos[:, 2] < drawer_grasp_pos[:, 2],
around_handle_reward + 0.5, around_handle_reward), around_handle_reward)
# reward for distance of each finger from the drawer
finger_dist_reward = torch.zeros_like(rot_reward)
lfinger_dist = torch.abs(franka_lfinger_pos[:, 2] - drawer_grasp_pos[:, 2])
rfinger_dist = torch.abs(franka_rfinger_pos[:, 2] - drawer_grasp_pos[:, 2])
finger_dist_reward = torch.where(franka_lfinger_pos[:, 2] > drawer_grasp_pos[:, 2],
torch.where(franka_rfinger_pos[:, 2] < drawer_grasp_pos[:, 2],
(0.04 - lfinger_dist) + (0.04 - rfinger_dist), finger_dist_reward), finger_dist_reward)
# regularization on the actions (summed for each environment)
action_penalty = torch.sum(actions ** 2, dim=-1)
# how far the cabinet has been opened out
# drawer_top_joint
open_reward = cabinet_dof_pos[:, 3] * \
around_handle_reward + cabinet_dof_pos[:, 3]
rewards = dist_reward_scale * dist_reward + rot_reward_scale * rot_reward \
+ around_handle_reward_scale * around_handle_reward + open_reward_scale * open_reward \
+ finger_dist_reward_scale * finger_dist_reward - \
action_penalty_scale * action_penalty
# bonus for opening drawer properly
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.01, rewards + 0.5, rewards)
rewards = torch.where(
cabinet_dof_pos[:, 3] > 0.2, rewards + around_handle_reward, rewards)
rewards = torch.where(
cabinet_dof_pos[:, 3] > 0.39, rewards + (2.0 * around_handle_reward), rewards)
# prevent bad style in opening drawer
rewards = torch.where(franka_lfinger_pos[:, 0] < drawer_grasp_pos[:, 0] - distX_offset,
torch.ones_like(rewards) * -1, rewards)
rewards = torch.where(franka_rfinger_pos[:, 0] < drawer_grasp_pos[:, 0] - distX_offset,
torch.ones_like(rewards) * -1, rewards)
# reset if drawer is open or max length reached
reset_buf = torch.where(
cabinet_dof_pos[:, 3] > 0.39, torch.ones_like(reset_buf), reset_buf)
reset_buf = torch.where(
progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
return rewards, reset_buf
@torch.jit.script
def compute_grasp_transforms(hand_rot, hand_pos, franka_local_grasp_rot, franka_local_grasp_pos,
drawer_rot, drawer_pos, drawer_local_grasp_rot, drawer_local_grasp_pos
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]
global_franka_rot, global_franka_pos = tf_combine(
hand_rot, hand_pos, franka_local_grasp_rot, franka_local_grasp_pos)
global_drawer_rot, global_drawer_pos = tf_combine(
drawer_rot, drawer_pos, drawer_local_grasp_rot, drawer_local_grasp_pos)
return global_franka_rot, global_franka_pos, global_drawer_rot, global_drawer_pos