#
# MIT License
#
# Copyright (c) 2020-2021 NVIDIA CORPORATION.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.#
import torch
import torch.autograd.profiler as profiler
from ...differentiable_robot_model.coordinate_transform import matrix_to_quaternion, quaternion_to_matrix
from ..cost import DistCost, PoseCost, ZeroCost, FiniteDifferenceCost
from ...mpc.rollout.arm_base import ArmBase
[docs]class ArmReacher(ArmBase):
"""
This rollout function is for reaching a cartesian pose for a robot
Todo:
1. Update exp_params to be kwargs
"""
def __init__(self, exp_params, tensor_args={'device':"cpu", 'dtype':torch.float32}, world_params=None):
super(ArmReacher, self).__init__(exp_params=exp_params,
tensor_args=tensor_args,
world_params=world_params)
self.goal_state = None
self.goal_ee_pos = None
self.goal_ee_rot = None
device = self.tensor_args['device']
float_dtype = self.tensor_args['dtype']
self.dist_cost = DistCost(**self.exp_params['cost']['joint_l2'], device=device,float_dtype=float_dtype)
self.goal_cost = PoseCost(**exp_params['cost']['goal_pose'],
tensor_args=self.tensor_args)
[docs] def cost_fn(self, state_dict, action_batch, no_coll=False, horizon_cost=True, return_dist=False):
cost = super(ArmReacher, self).cost_fn(state_dict, action_batch, no_coll, horizon_cost)
ee_pos_batch, ee_rot_batch = state_dict['ee_pos_seq'], state_dict['ee_rot_seq']
state_batch = state_dict['state_seq']
goal_ee_pos = self.goal_ee_pos
goal_ee_rot = self.goal_ee_rot
retract_state = self.retract_state
goal_state = self.goal_state
goal_cost, rot_err_norm, goal_dist = self.goal_cost.forward(ee_pos_batch, ee_rot_batch,
goal_ee_pos, goal_ee_rot)
cost += goal_cost
# joint l2 cost
if(self.exp_params['cost']['joint_l2']['weight'] > 0.0 and goal_state is not None):
disp_vec = state_batch[:,:,0:self.n_dofs] - goal_state[:,0:self.n_dofs]
cost += self.dist_cost.forward(disp_vec)
if(return_dist):
return cost, rot_err_norm, goal_dist
if self.exp_params['cost']['zero_acc']['weight'] > 0:
cost += self.zero_acc_cost.forward(state_batch[:, :, self.n_dofs*2:self.n_dofs*3], goal_dist=goal_dist)
if self.exp_params['cost']['zero_vel']['weight'] > 0:
cost += self.zero_vel_cost.forward(state_batch[:, :, self.n_dofs:self.n_dofs*2], goal_dist=goal_dist)
return cost
[docs] def update_params(self, retract_state=None, goal_state=None, goal_ee_pos=None, goal_ee_rot=None, goal_ee_quat=None):
"""
Update params for the cost terms and dynamics model.
goal_state: n_dofs
goal_ee_pos: 3
goal_ee_rot: 3,3
goal_ee_quat: 4
"""
super(ArmReacher, self).update_params(retract_state=retract_state)
if(goal_ee_pos is not None):
self.goal_ee_pos = torch.as_tensor(goal_ee_pos, **self.tensor_args).unsqueeze(0)
self.goal_state = None
if(goal_ee_rot is not None):
self.goal_ee_rot = torch.as_tensor(goal_ee_rot, **self.tensor_args).unsqueeze(0)
self.goal_ee_quat = matrix_to_quaternion(self.goal_ee_rot)
self.goal_state = None
if(goal_ee_quat is not None):
self.goal_ee_quat = torch.as_tensor(goal_ee_quat, **self.tensor_args).unsqueeze(0)
self.goal_ee_rot = quaternion_to_matrix(self.goal_ee_quat)
self.goal_state = None
if(goal_state is not None):
self.goal_state = torch.as_tensor(goal_state, **self.tensor_args).unsqueeze(0)
self.goal_ee_pos, self.goal_ee_rot = self.dynamics_model.robot_model.compute_forward_kinematics(self.goal_state[:,0:self.n_dofs], self.goal_state[:,self.n_dofs:2*self.n_dofs], link_name=self.exp_params['model']['ee_link_name'])
self.goal_ee_quat = matrix_to_quaternion(self.goal_ee_rot)
return True