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from pathlib import Path
import open3d as o3d
from threadpoolctl import threadpool_limits
import multiprocess as mp
import threading
from threading import Lock
import os
import numpy as np
from copy import deepcopy
from functools import partial
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
import transforms3d
import kornia
from pgnd.utils import get_root
root: Path = get_root(__file__)
from modules_planning.dynamics_module import DynamicsModule, fps
from utils.planning_utils import batch_chamfer_dist
class PlanningModule:
def __init__(self,
cfg,
device,
exp_root,
ckpt_path,
use_robot=False,
bimanual=True,
bbox=None,
eef_point=None,
debug=False,
):
super().__init__()
self.cfg = cfg
self.exp_root = exp_root
self.ckpt_path = ckpt_path
self.bimanual = bimanual
self.use_robot = use_robot
self.debug = debug
self.eef_point = eef_point
self.torch_device = device
assert bbox is not None
self.bbox = torch.tensor(bbox, dtype=torch.float32, device=self.torch_device)
self.repeated_action = False # allow non-repetitive action
self.n_look_ahead = 10 # num_steps_total
self.n_sample = 32
self.n_sample_chunk = 32
self.n_chunk = np.ceil(self.n_sample / self.n_sample_chunk).astype(int)
self.n_update_iter = 20
self.reward_weight = 10.0
self.target_state = torch.empty(0)
self.state = None
self.pts = torch.empty(0)
self.eef_xyz = torch.empty(0) # (n_grippers, 3)
self.eef_rot = torch.empty(0) # (n_grippers, 3, 3)
self.eef_gripper = torch.empty(0) # (n_grippers,)
self.dynamics_module = DynamicsModule(cfg, exp_root=exp_root, ckpt_path=ckpt_path, batch_size=self.n_sample_chunk, num_steps_total=self.n_look_ahead)
self.dynamics_module.reset_model(self.n_look_ahead)
self.xyz_noise_level = 0.002
self.quat_noise_level = 0.001
self.gripper_noise_level = 0.0
def set_target(self, pcd_path):
pcd = o3d.io.read_point_cloud(pcd_path)
target_state = np.array(pcd.points)
if len(target_state) == 0:
print('target state is empty')
return
target_state = torch.tensor(target_state, dtype=torch.float32, device=self.torch_device)
fps_idx = fps(target_state, 1000, device=self.torch_device, random_start=False)
target_state = target_state[fps_idx]
self.target_state = target_state.clone()
self.dynamics_module.set_target_state(target_state)
def model_rollout(self, act_seqs, visualize_pv=False):
pts = self.pts.clone()
n_grippers = self.cfg.sim.num_grippers
n_sample = act_seqs.shape[0]
eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
eef_rot = act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(n_sample, self.n_look_ahead, n_grippers, 3, 3)
eef_gripper = act_seqs[:, :, n_grippers * (3 + 3 * 3):].reshape(n_sample, self.n_look_ahead, n_grippers, 1)
eef_xyz_now = self.eef_xyz.clone()
eef_rot_now = self.eef_rot.clone()
eef_gripper_now = self.eef_gripper.clone()
eef_xyz = torch.cat([eef_xyz_now.repeat(n_sample, 1, 1, 1), eef_xyz], dim=1) # (n_sample, n_look_forward + 1, n_grippers, 3)
eef_rot = torch.cat([eef_rot_now.repeat(n_sample, 1, 1, 1, 1), eef_rot], dim=1) # (n_sample, n_look_forward + 1, n_grippers, 3, 3)
eef_gripper = torch.cat([eef_gripper_now[:, None].repeat(n_sample, 1, 1, 1), eef_gripper], dim=1) # (n_sample, n_look_forward + 1, n_grippers, 1)
assert eef_xyz.shape[1] == eef_rot.shape[1] == eef_gripper.shape[1] == self.n_look_ahead + 1
x, v = self.dynamics_module.rollout(pts, eef_xyz, eef_rot, eef_gripper, pts_his=None, visualize_pv=visualize_pv) # (n_sample, n_look_forward, n_pts, 3)
model_out = {
'x': x,
}
return model_out
def sample_action_seq(self, act_seq, iter_index=0):
# get action
n_grippers = self.cfg.sim.num_grippers
eef_xyz = act_seq[:, :n_grippers * 3].reshape(self.n_look_ahead, n_grippers, 3)
eef_rot = act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(self.n_look_ahead, n_grippers, 3, 3)
eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot)
eef_gripper = act_seq[:, n_grippers * (3 + 3 * 3):].reshape(self.n_look_ahead, n_grippers, 1)
# add noise
n_sample = self.n_sample_chunk
if self.repeated_action: # default: linear repeating action
eef_xyz_delta = torch.randn((n_sample, eef_xyz.shape[1], eef_xyz.shape[2]), device=self.torch_device, dtype=torch.float32) * self.xyz_noise_level
eef_quat_delta = torch.randn((n_sample, eef_quat.shape[1], eef_quat.shape[2]), device=self.torch_device, dtype=torch.float32) * self.quat_noise_level
eef_gripper_delta = torch.randn((n_sample, eef_gripper.shape[1], eef_gripper.shape[2]), device=self.torch_device, dtype=torch.float32) * self.gripper_noise_level
eef_xyz_delta = eef_xyz_delta[:, None].repeat(1, self.n_look_ahead, 1, 1)
eef_quat_delta = eef_quat_delta[:, None].repeat(1, self.n_look_ahead, 1, 1)
eef_gripper_delta = eef_gripper_delta[:, None].repeat(1, self.n_look_ahead, 1, 1)
else: # segmented repeated action
n_parts = 4
eef_xyz_delta_list = []
eef_quat_delta_list = []
eef_gripper_delta_list = []
for p in range(n_parts):
p_len = self.n_look_ahead // n_parts if p < n_parts - 1 else self.n_look_ahead - (n_parts - 1) * (self.n_look_ahead // n_parts)
eef_xyz_delta = torch.randn((n_sample, eef_xyz.shape[1], eef_xyz.shape[2]), device=self.torch_device, dtype=torch.float32) * self.xyz_noise_level * (1. / (iter_index + 1) ** 1) # TODO
eef_quat_delta = torch.randn((n_sample, eef_quat.shape[1], eef_quat.shape[2]), device=self.torch_device, dtype=torch.float32) * self.quat_noise_level * (1. / (iter_index + 1) ** 1)
eef_gripper_delta = torch.randn((n_sample, eef_gripper.shape[1], eef_gripper.shape[2]), device=self.torch_device, dtype=torch.float32) * self.gripper_noise_level * (1. / (iter_index + 1) ** 1)
eef_xyz_delta = eef_xyz_delta[:, None].repeat(1, p_len, 1, 1)
eef_quat_delta = eef_quat_delta[:, None].repeat(1, p_len, 1, 1)
eef_gripper_delta = eef_gripper_delta[:, None].repeat(1, p_len, 1, 1)
eef_xyz_delta_list.append(eef_xyz_delta)
eef_quat_delta_list.append(eef_quat_delta)
eef_gripper_delta_list.append(eef_gripper_delta)
eef_xyz_delta = torch.cat(eef_xyz_delta_list, dim=1)
eef_quat_delta = torch.cat(eef_quat_delta_list, dim=1)
eef_gripper_delta = torch.cat(eef_gripper_delta_list, dim=1)
eef_xyz_delta_cum = torch.cumsum(eef_xyz_delta, dim=1) # (n_sample, n_look_ahead, n_grippers, 3)
eef_quat_delta_cum = torch.cumsum(eef_quat_delta, dim=1) # (n_sample, n_look_ahead, n_grippers, 4)
eef_gripper_delta_cum = torch.cumsum(eef_gripper_delta, dim=1) # (n_sample, n_look_ahead, n_grippers, 1)
eef_xyz = eef_xyz[None] + eef_xyz_delta_cum
eef_quat = eef_quat[None] + eef_quat_delta_cum
eef_gripper = eef_gripper[None] + eef_gripper_delta_cum
eef_quat = eef_quat / (torch.norm(eef_quat, dim=-1, keepdim=True) + 1e-6) # normalize
eef_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat)
act_seqs = torch.zeros((n_sample, self.n_look_ahead, n_grippers * (3 + 9 + 1)), device=self.torch_device, dtype=torch.float32)
act_seqs[:, :, :n_grippers * 3] = eef_xyz.reshape(n_sample, self.n_look_ahead, -1)
act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(n_sample, self.n_look_ahead, -1)
act_seqs[:, :, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(n_sample, self.n_look_ahead, -1)
self.clip_actions(act_seqs)
return act_seqs
def evaluate_traj(self, model_out, act_seqs):
target_state = self.target_state.clone()
x = model_out['x'] # (n_sample, n_look_forward, n_pts, 3)
chamfer_distance = batch_chamfer_dist(x[:, -1], target_state) # (n_sample,)
curr_chamfer_distance = batch_chamfer_dist(x[:, 0], target_state) # (n_sample,)
print('curr chamfer_distance:', curr_chamfer_distance.min().item(), end=' ')
print('best chamfer_distance:', chamfer_distance.min().item())
n_sample = self.n_sample_chunk
n_grippers = self.cfg.sim.num_grippers
assert act_seqs.shape[0] == n_sample
eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
if eef_xyz.shape[2] == 2:
eef_xyz_left = eef_xyz[:, :, 0] # (n_sample, self.n_look_ahead, 3)
eef_xyz_right = eef_xyz[:, :, 1] # (n_sample, self.n_look_ahead, 3)
eef_xyz_dist = torch.norm(eef_xyz_left - eef_xyz_right, dim=-1) # (n_sample, self.n_look_ahead)
eef_xyz_dist_penalty = (eef_xyz_dist.max(dim=1).values > 0.3).float() * 100.0 # (n_sample,) # the smaller the better # TODO distance threshold
eef_height_penalty = torch.logical_or(eef_xyz_left[:, :, 2].max(dim=1).values > -0.02, eef_xyz_right[:, :, 2].max(dim=1).values > -0.02).to(torch.float32) * 100.0 # (n_sample,) # the smaller the better
eef_height_penalty += torch.logical_or(
(eef_xyz_left.max(dim=1).values > (self.bbox[:, 1] - 0.02)).any(dim=-1),
(eef_xyz_left.min(dim=1).values < (self.bbox[:, 0] + 0.02)).any(dim=-1)
).to(torch.float32) * 100.0 # (n_sample,) # the smaller the better
eef_height_penalty += torch.logical_or(
(eef_xyz_right.max(dim=1).values > (self.bbox[:, 1] - 0.02)).any(dim=-1),
(eef_xyz_right.min(dim=1).values < (self.bbox[:, 0] + 0.02)).any(dim=-1)
).to(torch.float32) * 100.0 # (n_sample,) # the smaller the better
reward = -chamfer_distance - eef_xyz_dist_penalty - eef_height_penalty # to maximize
else:
assert eef_xyz.shape[2] == 1
eef_xyz_dist_penalty = 0
eef_height_penalty = (eef_xyz[:, :, 0, 2].max(dim=1).values > -0.02).to(torch.float32) * 100.0 # (n_sample,) # the smaller the better
eef_height_penalty += torch.logical_or(
(eef_xyz[:, :, 0].max(dim=1).values > (self.bbox[:, 1] - 0.02)).any(dim=-1),
(eef_xyz[:, :, 0].min(dim=1).values < (self.bbox[:, 0] + 0.02)).any(dim=-1)
).to(torch.float32) * 100.0 # (n_sample,) # the smaller the better
reward = -chamfer_distance - eef_xyz_dist_penalty - eef_height_penalty # to maximize
print('best reward:', reward.max().item())
eval_out = {
'reward_seqs': reward,
}
return eval_out
def optimize_action_mppi(self, act_seqs, reward_seqs):
weight_seqs = F.softmax(reward_seqs * self.reward_weight, dim=0) # (n_sample,)
assert len(weight_seqs.shape) == 1 and weight_seqs.shape[0] == self.n_sample_chunk
n_sample = self.n_sample_chunk
n_grippers = self.cfg.sim.num_grippers
assert act_seqs.shape[0] == n_sample
eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
eef_rot = act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(n_sample, self.n_look_ahead, n_grippers, 3, 3)
eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot)
eef_gripper = act_seqs[:, :, n_grippers * (3 + 3 * 3):].reshape(n_sample, self.n_look_ahead, n_grippers, 1)
eef_xyz = torch.sum(weight_seqs[:, None, None, None] * eef_xyz, dim=0) # (n_look_ahead, n_grippers, 3)
eef_gripper = torch.sum(weight_seqs[:, None, None, None] * eef_gripper, dim=0) # (n_look_ahead, n_grippers, 1)
eef_quat = torch.sum(weight_seqs[:, None, None, None] * eef_quat, dim=0) # (n_look_ahead, n_grippers, 4)
eef_quat = eef_quat / (torch.norm(eef_quat, dim=-1, keepdim=True) + 1e-6) # normalize
eef_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat)
act_seq = torch.zeros((self.n_look_ahead, n_grippers * (3 + 9 + 1)), device=self.torch_device, dtype=torch.float32)
act_seq[:, :n_grippers * 3] = eef_xyz.reshape(self.n_look_ahead, -1)
act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(self.n_look_ahead, -1)
act_seq[:, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(self.n_look_ahead, -1)
act_seq = self.clip_actions(act_seq[None])[0]
return act_seq
def clip_actions(self, act_seqs):
no_sample_dim = False
if len(act_seqs.shape) == 2:
no_sample_dim = True
act_seqs = act_seqs[None]
n_sample = act_seqs.shape[0]
n_grippers = self.cfg.sim.num_grippers
eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
eef_rot = act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(n_sample, self.n_look_ahead, n_grippers, 3, 3)
eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot) # (n_sample, n_look_ahead, n_grippers, 4)
eef_gripper = act_seqs[:, :, n_grippers * (3 + 3 * 3):].reshape(n_sample, self.n_look_ahead, n_grippers, 1)
eef_xyz = torch.clamp(eef_xyz, self.bbox[:, 0], self.bbox[:, 1])
eef_aa = kornia.geometry.conversions.quaternion_to_axis_angle(eef_quat) # (n_sample, n_look_ahead, n_grippers, 3)
max_rad = 1.0
eef_aa_mask = torch.norm(eef_aa, dim=-1) > max_rad # (n_sample, n_look_ahead, n_grippers)
eef_aa[eef_aa_mask] = eef_aa[eef_aa_mask] / torch.norm(eef_aa[eef_aa_mask], dim=-1, keepdim=True) * max_rad # cannot exceed 0.5 rad
eef_quat = kornia.geometry.conversions.axis_angle_to_quaternion(eef_aa)
eef_quat = eef_quat / (torch.norm(eef_quat, dim=-1, keepdim=True) + 1e-6) # normalize
eef_gripper = torch.clamp(eef_gripper, 0.0, 1.0)
eef_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat)
act_seqs[:, :, :n_grippers * 3] = eef_xyz.reshape(n_sample, self.n_look_ahead, -1)
act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(n_sample, self.n_look_ahead, -1)
act_seqs[:, :, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(n_sample, self.n_look_ahead, -1)
if no_sample_dim:
assert act_seqs.shape[0] == 1
act_seqs = act_seqs[0]
return act_seqs
def get_command(self, state, save_dir=None, is_first_iter=False):
self.state = state
best_act_seq = None
best_reward_seq = None
pts = state["perception_out"]["value"]["pts"].copy()
pts = np.concatenate(pts, axis=0)
# remove outliers
rm_outliers = False
if rm_outliers:
pcd_rm = o3d.geometry.PointCloud()
pcd_rm.points = o3d.utility.Vector3dVector(pts)
outliers = None
new_outlier = None
rm_iter = 0
while new_outlier is None or len(new_outlier.points) > 0:
_, inlier_idx = pcd_rm.remove_statistical_outlier(
nb_neighbors = 30, std_ratio = 2.5 + rm_iter * 0.5
)
new_pcd = pcd_rm.select_by_index(inlier_idx)
new_outlier = pcd_rm.select_by_index(inlier_idx, invert=True)
if outliers is None:
outliers = new_outlier
else:
outliers += new_outlier
pcd_rm = new_pcd
rm_iter += 1
pts = np.array(pcd_rm.points)
pts = torch.tensor(pts, dtype=torch.float32, device=self.torch_device)
if is_first_iter:
self.dynamics_module.reset_preprocess_meta(pts)
self.dynamics_module.reset_downsample_indices(pts)
self.pts = pts
if save_dir is not None:
os.makedirs(save_dir, exist_ok=True)
chamfer_now = batch_chamfer_dist(pts[None], self.target_state.clone())
print('chamfer_now:', chamfer_now.item())
with open(Path(save_dir) / 'chamfer.txt', 'w') as f:
f.write(str(chamfer_now.item()))
pts_save = pts.cpu().numpy()
o3d_pts = o3d.geometry.PointCloud()
o3d_pts.points = o3d.utility.Vector3dVector(pts_save)
o3d.io.write_point_cloud(str(Path(save_dir) / 'pts.ply'), o3d_pts)
target_state_save = self.target_state.cpu().numpy()
o3d_target_state = o3d.geometry.PointCloud()
o3d_target_state.points = o3d.utility.Vector3dVector(target_state_save)
o3d.io.write_point_cloud(str(Path(save_dir) / 'target_state.ply'), o3d_target_state)
if self.bimanual:
left_robot_out = state["robot_out"]["left_value"]
left_gripper_out = state["gripper_out"]["left_value"]
right_robot_out = state["robot_out"]["right_value"]
right_gripper_out = state["gripper_out"]["right_value"]
left_robot_out = torch.tensor(left_robot_out, dtype=torch.float32, device=self.torch_device)
left_gripper_out = torch.tensor(left_gripper_out, dtype=torch.float32, device=self.torch_device)
right_robot_out = torch.tensor(right_robot_out, dtype=torch.float32, device=self.torch_device)
right_gripper_out = torch.tensor(right_gripper_out, dtype=torch.float32, device=self.torch_device)
robot_out = None
gripper_out = None
else:
robot_out = state["robot_out"]["value"]
gripper_out = state["gripper_out"]["value"]
robot_out = torch.tensor(robot_out, dtype=torch.float32, device=self.torch_device)
gripper_out = torch.tensor(gripper_out, dtype=torch.float32, device=self.torch_device)
left_robot_out = None
left_gripper_out = None
right_robot_out = None
right_gripper_out = None
if self.bimanual:
b2w_l = state["b2w_l"]
b2w_r = state["b2w_r"]
b2w_l = torch.tensor(b2w_l, dtype=torch.float32, device=self.torch_device)
b2w_r = torch.tensor(b2w_r, dtype=torch.float32, device=self.torch_device)
b2w = None
else:
b2w = state["b2w"]
b2w = torch.tensor(b2w, dtype=torch.float32, device=self.torch_device)
b2w_l = None
b2w_r = None
# construct act_seq using current robot state
# assert not self.cfg.sim.gripper_points
eef_xyz = torch.zeros((self.n_look_ahead + 1, self.cfg.sim.num_grippers, 3), device=self.torch_device)
eef_quat = torch.zeros((self.n_look_ahead + 1, self.cfg.sim.num_grippers, 4), device=self.torch_device)
eef_quat[:, :, 0] = 1.0
eef_gripper = torch.zeros((self.n_look_ahead + 1, 1), device=self.torch_device)
# construct eef_world
eef_points = torch.tensor([[0.0, 0.0, 0.175, 1]], device=self.torch_device) # the eef point in the gripper frame
eef_axis = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]], dtype=torch.float32, device=self.torch_device) # (3, 4)
if self.bimanual:
left_eef_world_list = []
right_eef_world_list = []
assert left_robot_out is not None and right_robot_out is not None
assert b2w_l is not None and b2w_r is not None
for e2b, b2w, eef_world_list in zip([left_robot_out, right_robot_out], [b2w_l, b2w_r], [left_eef_world_list, right_eef_world_list]):
eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3] # (n, 3)
# eef_points_vis.append(eef_points)
# eef_points_world_vis.append(eef_points_world)
eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T # (3, 3)
eef_world = torch.cat([eef_points_world, eef_orientation_world], dim=0) # (n+3, 3)
eef_world_list.append(eef_world)
left_eef_world = torch.cat(left_eef_world_list, dim=0) # (n+3, 3)
right_eef_world = torch.cat(right_eef_world_list, dim=0) # (n+3, 3)
eef_world = torch.cat([left_eef_world, right_eef_world], dim=0) # (2n+6, 3)
else:
assert robot_out is not None
assert b2w is not None
e2b = robot_out # (4, 4)
eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3] # (n, 3)
# eef_points_vis.append(eef_points)
# eef_points_world_vis.append(eef_points_world)
eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T # (3, 3)
eef_world = torch.cat([eef_points_world, eef_orientation_world], dim=0) # (n+3, 3)
if self.bimanual:
assert left_gripper_out is not None and right_gripper_out is not None
gripper_world = torch.tensor([left_gripper_out, right_gripper_out, 0.0], device=self.torch_device)[None, :] # (1, 3)
else:
assert gripper_out is not None
gripper_world = torch.tensor([gripper_out, 0.0, 0.0], device=self.torch_device)[None, :] # (1, 3)
eef_world = torch.cat([eef_world, gripper_world], dim=0) # (n+4, 3) or (2n+7, 3)
robot = eef_world
# decode eef_world
if len(robot.shape) > 1: # 5 or 9
assert robot.shape[0] in [5, 9] # bi-manual (2 * (1 pos + 3 rot) + 1 gripper) or single arm (1 pos + 3 rot + 1 gripper or 1 pos)
gripper = robot[-1]
robot = robot[:-1]
robot = robot.reshape(-1, 4, 3)
robot_trans = robot[:, 0] # (n, 3)
robot_rot = robot[:, 1:] # (n, 3, 3)
if robot_trans.shape[0] == 1: # single arm
gripper = gripper[:1] # (1,)
else: # bi-manual
gripper = gripper[:2] # (2,)
else:
assert len(robot.shape) == 1 and robot.shape[0] == 3
robot_trans = robot
robot_rot = torch.eye(3).to(self.torch_device).to(torch.float32)
gripper = torch.tensor([0.0], device=self.torch_device).to(torch.float32)
robot_trans = robot_trans # + torch.tensor([0, 0, -0.01], device=self.torch_device) # offset
gripper = torch.clamp(gripper / 800.0, 0, 1) # 1: open, 0: close
# init eef variables in world frame
eef_xyz = robot_trans.reshape(-1, 3)
eef_rot = robot_rot.reshape(-1, 3, 3) # (n_grippers, 3, 3)
eef_gripper = gripper.reshape(-1) # (n_grippers,)
assert eef_xyz.shape[0] == eef_rot.shape[0] == eef_gripper.shape[0]
n_grippers = eef_xyz.shape[0]
self.eef_xyz = eef_xyz
self.eef_rot = eef_rot
self.eef_gripper = eef_gripper
# init act_seq in world frame
act_seq = torch.zeros((self.n_look_ahead, n_grippers * (3 + 9 + 1)), device=self.torch_device)
act_seq[:, :n_grippers * 3] = eef_xyz.reshape(-1)
act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(-1)
act_seq[:, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(-1)
res_all = []
for ci in range(self.n_chunk):
best_act_seq = act_seq
for ti in range(self.n_update_iter):
print(f'chunk: {ci}/{self.n_chunk}, iter: {ti}/{self.n_update_iter}')
with torch.no_grad():
act_seqs = self.sample_action_seq(act_seq, iter_index=ti) # support iteration-dependent noise
model_out = self.model_rollout(act_seqs, visualize_pv=False) # TODO
eval_out = self.evaluate_traj(model_out, act_seqs)
reward_seqs = eval_out["reward_seqs"] # (n_sample,)
act_seq = self.optimize_action_mppi(act_seqs, reward_seqs)
best_reward_idx = torch.argmax(reward_seqs)
if ti == 0:
best_act_seq = act_seqs[best_reward_idx]
best_reward_seq = reward_seqs[best_reward_idx]
elif reward_seqs[best_reward_idx] > best_reward_seq:
best_act_seq = act_seqs[best_reward_idx]
best_reward_seq = reward_seqs[best_reward_idx]
# model_out = self.model_rollout(best_act_seq[None].repeat(self.n_sample_chunk, 1, 1), visualize_pv=True) # TODO
torch.cuda.empty_cache()
# model_out = self.model_rollout(best_act_seq[None].repeat(self.n_sample_chunk, 1, 1), visualize_pv=True) # TODO
res = {
"best_act_seq": best_act_seq, # (n_look_ahead, n_grippers * (3 + 9 + 1))
"best_reward_seq": best_reward_seq,
}
res_all.append(res)
reward_list = [res["best_reward_seq"].item() for res in res_all]
best_idx = np.argmax(reward_list)
best_act_seq = res_all[best_idx]['best_act_seq'] # (n_look_ahead, n_grippers * (3 + 9 + 1))
torch.cuda.empty_cache()
eef_xyz = best_act_seq[:, :n_grippers * 3].reshape(self.n_look_ahead, n_grippers, 3)
eef_rot = best_act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(self.n_look_ahead, n_grippers, 3, 3)
eef_gripper = best_act_seq[:, n_grippers * (3 + 3 * 3):].reshape(self.n_look_ahead, n_grippers, 1)
# transform back into robot coordinates
eef_points = torch.tensor([[0.0, 0.0, 0.175, 1]], device=self.torch_device) # the eef point in the gripper frame
eef_axis = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]], dtype=torch.float32, device=self.torch_device) # (3, 4)
# initialize command
command = [[] for _ in range(eef_xyz.shape[-2])] # (n_grippers,)
e2b_command = [[] for _ in range(eef_xyz.shape[-2])] # (n_grippers,)
look_ahead_range = range(self.n_look_ahead)
for li in look_ahead_range:
if self.bimanual:
assert b2w_l is not None and b2w_r is not None
left_eef_xyz = eef_xyz[li:li+1, 0] # (1, 3)
right_eef_xyz = eef_xyz[li:li+1, 1] # (1, 3)
left_eef_rot = eef_rot[li, 0] # (3, 3)
right_eef_rot = eef_rot[li, 1] # (3, 3)
e2b_l = torch.eye(4, device=self.torch_device)
e2b_r = torch.eye(4, device=self.torch_device)
for b2w, e2b, eef_points_world, eef_orientation_world in \
zip([b2w_l, b2w_r], [e2b_l, e2b_r], [left_eef_xyz, right_eef_xyz], [left_eef_rot, right_eef_rot]):
eef_orientation_world = eef_orientation_world.T # (3, 3) = b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T
eef_orientation_world = b2w[:3, :3].T @ eef_orientation_world # (3, 3) = e2b[:3, :3] @ eef_axis[:, :3].T
e2b[:3, :3] = eef_orientation_world @ eef_axis[:, :3] # (3, 3) = e2b[:3, :3]
eef_points_world = eef_points_world.T # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t) + b2w_t
eef_points_world = eef_points_world - b2w[:3, 3].reshape(-1, 1) # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t)
eef_points_world = b2w[:3, :3].T @ eef_points_world # (3, n) = e2b_R @ eef_points[:, :3].T + e2b_t
e2b[:3, 3:4] = eef_points_world - e2b[:3, :3] @ eef_points[:, :3].T # (3, n) = e2b_t
e2b_list = [e2b_l, e2b_r]
else:
assert b2w is not None
eef_points_world = eef_xyz[li:li+1, 0] # (n, 3)
eef_orientation_world = eef_rot[li, 0]
e2b = torch.eye(4, device=self.torch_device)
eef_orientation_world = eef_orientation_world.T # (3, 3) = b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T
eef_orientation_world = b2w[:3, :3].T @ eef_orientation_world # (3, 3) = e2b[:3, :3] @ eef_axis[:, :3].T
e2b[:3, :3] = eef_orientation_world @ eef_axis[:, :3] # (3, 3) = e2b[:3, :3]
eef_points_world = eef_points_world.T # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t) + b2w_t
eef_points_world = eef_points_world - b2w[:3, 3].reshape(-1, 1) # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t)
eef_points_world = b2w[:3, :3].T @ eef_points_world # (3, n) = e2b_R @ eef_points[:, :3].T + e2b_t
e2b[:3, 3:4] = eef_points_world - e2b[:3, :3] @ eef_points[:, :3].T # (3, n) = e2b_t
e2b_list = [e2b]
for gripper_id in range(eef_xyz.shape[-2]):
fk_trans_mat = e2b_list[gripper_id].cpu().numpy()
cur_xyzrpy = np.zeros(6)
cur_xyzrpy[:3] = fk_trans_mat[:3, 3] * 1000
cur_xyzrpy[3:] = transforms3d.euler.mat2euler(fk_trans_mat[:3, :3])
cur_xyzrpy[3:] = cur_xyzrpy[3:] / np.pi * 180
gripper = eef_gripper[li, gripper_id].item()
gripper = gripper * 800.0
single_command = list(cur_xyzrpy) + [gripper]
command[gripper_id].append(single_command)
# debug
e2b_command[gripper_id].append(e2b_list[gripper_id].cpu().numpy())
return command
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