from typing import Any, Dict, Union import numpy as np import sapien import torch import mani_skill.envs.utils.randomization as randomization from mani_skill.agents.robots import SO100, Fetch, Panda from mani_skill.envs.sapien_env import BaseEnv from mani_skill.envs.tasks.tabletop.pick_cube_cfgs import PICK_CUBE_CONFIGS from mani_skill.sensors.camera import CameraConfig from mani_skill.utils import sapien_utils from mani_skill.utils.building import actors from mani_skill.utils.registration import register_env from mani_skill.utils.scene_builder.table import TableSceneBuilder from mani_skill.utils.structs.pose import Pose #Robomme import matplotlib.pyplot as plt import random from mani_skill.utils.geometry.rotation_conversions import ( euler_angles_to_matrix, matrix_to_quaternion, ) from .utils import * from .utils.SceneGenerationError import SceneGenerationError from .utils.subgoal_evaluate_func import static_check from .utils.object_generation import spawn_fixed_cube, build_board_with_hole from .utils import reset_panda from .utils.difficulty import normalize_robomme_difficulty from ..logging_utils import logger PICK_CUBE_DOC_STRING = """**Task Description:** A simple task where the objective is to grasp a red cube with the {robot_id} robot and move it to a target goal position. This is also the *baseline* task to test whether a robot with manipulation capabilities can be simulated and trained properly. Hence there is extra code for some robots to set them up properly in this environment as well as the table scene builder. **Randomizations:** - the cube's xy position is randomized on top of a table in the region [0.1, 0.1] x [-0.1, -0.1]. It is placed flat on the table - the cube's z-axis rotation is randomized to a random angle - the target goal position (marked by a green sphere) of the cube has its xy position randomized in the region [0.1, 0.1] x [-0.1, -0.1] and z randomized in [0, 0.3] **Success Conditions:** - the cube position is within `goal_thresh` (default 0.025m) euclidean distance of the goal position - the robot is static (q velocity < 0.2) """ @register_env("VideoPlaceButton") class VideoPlaceButton(BaseEnv): _sample_video_link = "https://github.com/haosulab/ManiSkill/raw/main/figures/environment_demos/PickCube-v1_rt.mp4" SUPPORTED_ROBOTS = [ "panda", "fetch", "xarm6_robotiq", "so100", "widowxai", ] agent: Union[Panda] goal_thresh = 0.025 cube_spawn_half_size = 0.05 cube_spawn_center = (0, 0) config_easy = { 'color': 1, "additional_place":False, "swap":False, "targets":3 } config_medium= { 'color': 3, "additional_place":False, "swap":False, "targets":4 } config_hard = { 'color': 3, "additional_place":False, "swap":True, "targets":4 } # Combine into a dictionary configs = { 'hard': config_hard, 'easy': config_easy, 'medium': config_medium } def __init__(self, *args, robot_uids="panda_wristcam", robot_init_qpos_noise=0,seed=0,Robomme_video_episode=None,Robomme_video_path=None, **kwargs): self.use_demonstrationwrapper=False self.demonstration_record_traj=False self.robot_init_qpos_noise = robot_init_qpos_noise if robot_uids in PICK_CUBE_CONFIGS: cfg = PICK_CUBE_CONFIGS[robot_uids] else: cfg = PICK_CUBE_CONFIGS["panda"] self.cube_half_size = cfg["cube_half_size"] self.goal_thresh = cfg["goal_thresh"] self.cube_spawn_half_size = cfg["cube_spawn_half_size"] self.cube_spawn_center = cfg["cube_spawn_center"] self.max_goal_height = cfg["max_goal_height"] self.sensor_cam_eye_pos = cfg["sensor_cam_eye_pos"] self.sensor_cam_target_pos = cfg["sensor_cam_target_pos"] self.human_cam_eye_pos = cfg["human_cam_eye_pos"] self.human_cam_target_pos = cfg["human_cam_target_pos"] self.seed = seed self._episode_rng = torch.Generator() self._episode_rng.manual_seed(seed) self.robomme_failure_recovery = bool( kwargs.pop("robomme_failure_recovery", False) ) self.robomme_failure_recovery_mode = kwargs.pop( "robomme_failure_recovery_mode", None ) if isinstance(self.robomme_failure_recovery_mode, str): self.robomme_failure_recovery_mode = ( self.robomme_failure_recovery_mode.lower() ) normalized_robomme_difficulty = normalize_robomme_difficulty( kwargs.pop("difficulty", None) ) if normalized_robomme_difficulty is not None: self.difficulty = normalized_robomme_difficulty else: # Determine difficulty based on seed % 3 seed_mod = seed % 3 if seed_mod == 0: self.difficulty = "easy" elif seed_mod == 1: self.difficulty = "medium" else: # seed_mod == 2 self.difficulty = "hard" # Use seed to randomly determine number of repetitions (1-5) generator = torch.Generator() generator.manual_seed(seed) self.onto_goalsite=False self.start_step=99999 self.end_step=99999 super().__init__(*args, robot_uids=robot_uids, **kwargs) @property def _default_sensor_configs(self): pose = sapien_utils.look_at( eye=self.sensor_cam_eye_pos, target=self.sensor_cam_target_pos ) camera_eye=[0.3,0,0.4] camera_target =[0,0,-0.2] pose = sapien_utils.look_at( eye=camera_eye, target=camera_target ) return [CameraConfig("base_camera", pose, 256, 256, np.pi / 2, 0.01, 100)] @property def _default_human_render_camera_configs(self): pose = sapien_utils.look_at( eye=self.human_cam_eye_pos, target=self.human_cam_target_pos ) return CameraConfig("render_camera", pose, 512, 512, 1, 0.01, 100) def _load_agent(self, options: dict): super()._load_agent(options, sapien.Pose(p=[-0.615, 0, 0])) def _load_scene(self, options: dict): generator = torch.Generator() generator.manual_seed(self.seed) try: self.table_scene = TableSceneBuilder( self, robot_init_qpos_noise=self.robot_init_qpos_noise ) self.table_scene.build() try: self.goal_site = spawn_random_target( self, avoid=None, # Use current avoidance list, containing all spawned cubes include_existing=False, # Manually maintain list include_goal=False, # Manually maintain list region_center=[-0.1, 0], region_half_size=0.1, radius=self.cube_half_size * 3, # Use radius instead of half_size thickness=0.005, # target thickness min_gap=self.cube_half_size * 1, # Gap requirement same as cube name_prefix=f"goal_site", generator=generator, ) except RuntimeError as exc: raise SceneGenerationError("goal_site sampling failed") from exc avoid = [self.goal_site] button_obb = build_button( self, center_xy=(0.1, 0), scale=1.5, generator=generator, randomize_range=(0.05, 0.3) ) avoid.append(button_obb) self.all_cubes = [] # Save all cube objects # Initialize storage for each color group self.red_cubes = [] self.red_cube_names = [] self.blue_cubes = [] self.blue_cube_names = [] self.green_cubes = [] self.green_cube_names = [] cubes_per_color = 1 color_groups = [ {"color": (1, 0, 0, 1), "name": "red", "list": self.red_cubes, "name_list": self.red_cube_names}, {"color": (0, 0, 1, 1), "name": "blue", "list": self.blue_cubes, "name_list": self.blue_cube_names}, {"color": (0, 1, 0, 1), "name": "green", "list": self.green_cubes, "name_list": self.green_cube_names}, ] shuffle_indices = torch.randperm(len(color_groups), generator=generator).tolist() color_groups = [color_groups[i] for i in shuffle_indices] self.target_color_name = color_groups[0]["name"] logger.debug(f"Target color selected: {self.target_color_name}") # Generate cubes for each color group for idx, group in enumerate(color_groups): if idx < self.configs[self.difficulty]["color"]: for cube_idx in range(cubes_per_color): try: cube = spawn_random_cube( self, color=group["color"], avoid=avoid, include_existing=False, include_goal=False, region_center=[0 , 0], region_half_size=0.2, half_size=self.cube_half_size, min_gap=self.cube_half_size, random_yaw=True, name_prefix=f"cube_{group['name']}_{cube_idx}", generator=generator, ) except RuntimeError as exc: raise SceneGenerationError( f"Failed to generate {group['name']} cube {cube_idx}: {exc}" ) from exc self.all_cubes.append(cube) group["list"].append(cube) cube_name = f"cube_{group['name']}_{cube_idx}" group["name_list"].append(cube_name) setattr(self, cube_name, cube) avoid.append(cube) logger.debug(f"Generated {len(group['list'])} {group['name']} cubes") logger.debug( f"Generated {len(self.all_cubes)} cubes total (red: {len(self.red_cubes)}, blue: {len(self.blue_cubes)}, green: {len(self.green_cubes)})" ) self.targets = [] for i in range(4): if i < self.configs[self.difficulty]["targets"]: try: target = spawn_random_target( self, avoid=avoid, # Use current avoidance list, containing all spawned cubes include_existing=False, # Manually maintain list include_goal=False, # Manually maintain list region_center=[0, 0], region_half_size=0.2, radius=self.cube_half_size * 2, # Use radius instead of half_size thickness=0.005, # target thickness min_gap=self.cube_half_size * 1, # Gap requirement same as cube name_prefix=f"target_{i}", generator=generator, ) except RuntimeError as exc: raise SceneGenerationError(f"Target {i + 1} sampling failed: {exc}") from exc self.targets.append(target) setattr(self, f"target_{i}", target) avoid.append(target) if len(self.all_cubes) > 0: target_cube_idx = torch.randint(0, len(self.all_cubes), (1,), generator=generator).item() self.target_cube = self.all_cubes[target_cube_idx] if self.target_cube in self.red_cubes: self.target_color_name = "red" elif self.target_cube in self.blue_cubes: self.target_color_name = "blue" elif self.target_cube in self.green_cubes: self.target_color_name = "green" logger.debug( f"Target cube selected: {self.target_color_name} cube (index {target_cube_idx} in all_cubes)" ) else: self.target_cube = None self.target_color_name = None logger.debug("No cubes generated, no target cube selected") self.non_target_cubes = [cube for cube in self.all_cubes if cube != self.target_cube] logger.debug(f"Non-target cubes: {len(self.non_target_cubes)}") self.swap_target_a = None self.swap_target_b = None self.swap_target_other = [] if self.configs[self.difficulty]["swap"] == True: if len(self.targets) >= 2: perm = torch.randperm(len(self.targets), generator=generator) swap_idx_a = perm[0].item() swap_idx_b = perm[1].item() self.swap_target_a = self.targets[swap_idx_a] self.swap_target_b = self.targets[swap_idx_b] self.swap_target_other = [ target for idx, target in enumerate(self.targets) if idx not in (swap_idx_a, swap_idx_b) ] logger.debug( f"Swap targets selected: target_{swap_idx_a} <-> target_{swap_idx_b}" ) if self.configs[self.difficulty]["additional_place"] == True: self.pre_flag = torch.rand(1, generator=generator).item() < 0.5 self.post_flag = torch.rand(1, generator=generator).item() < 0.5 else: self.pre_flag = 0 self.post_flag = 0 self.task_flag = torch.rand(1, generator=generator).item() < 0.5 if self.task_flag == 1: self.target_target = self.target_0 self.target_target_language = "before" else: self.target_target = self.target_1 self.target_target_language = "after" self.targets_not_true = [ t for i, t in enumerate(self.targets) if self.targets[i] != self.target_target ] tasks = [] if self.pre_flag == True: tasks.append( { "func": (lambda: is_obj_pickup(self, obj=self.target_cube)), "name": f"pick up the cube", "subgoal_segment": f"pick up the cube at <>", "choice_label": "pick up the cube", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_pickup( env, planner, obj=self.target_cube ), "segment": self.target_cube, } ) tasks.append( { "func": ( lambda: is_obj_dropped_onto( self, obj=self.target_cube, target=self.target_2 ) ), "name": "drop the cube onto target", "subgoal_segment": f"drop the cube onto target at <>", "choice_label": "drop onto", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_putonto_whenhold( env, planner, target=self.target_2 ), "segment": self.target_2, } ) tasks.append( { "func": (lambda: is_obj_pickup(self, obj=self.target_cube)), "name": f"pick up the cube", "subgoal_segment": f"pick up the cube at <>", "choice_label": "pick up the cube", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_pickup( env, planner, obj=self.target_cube ), "segment": self.target_cube, } ) tasks.append( { "func": ( lambda: is_obj_dropped_onto( self, obj=self.target_cube, target=self.target_0 ) ), "name": "drop the cube onto target", "subgoal_segment": f"drop the cube onto target at <>", "choice_label": "drop onto", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_putonto_whenhold( env, planner, target=self.target_0 ), "segment": self.target_0, } ) tasks.append( { "func": (lambda: is_button_pressed(self, obj=self.button)), "name": "press the button", "subgoal_segment": f"press the button at <>", "choice_label": "press the button", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_button(env, planner, self.button), "segment": self.cap_link, } ) if self.post_flag == True: tasks.append( { "func": (lambda: is_obj_pickup(self, obj=self.target_cube)), "name": f"pick up the cube", "subgoal_segment": f"pick up the cube at <>", "choice_label": "pick up the cube", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_pickup( env, planner, obj=self.target_cube ), "segment": self.target_cube, } ) tasks.append( { "func": ( lambda: is_obj_dropped_onto( self, obj=self.target_cube, target=self.target_3 ) ), "name": "drop the cube onto target", "subgoal_segment": f"drop the cube onto target at <>", "choice_label": "drop onto", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_putonto_whenhold( env, planner, target=self.target_3 ), "segment": self.target_3, } ) tasks.append( { "func": (lambda: is_obj_pickup(self, obj=self.target_cube)), "name": f"pick up the cube", "subgoal_segment": f"pick up the cube at <>", "choice_label": "pick up the cube", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_pickup( env, planner, obj=self.target_cube ), "segment": self.target_cube, } ) tasks.append( { "func": ( lambda: is_obj_dropped_onto( self, obj=self.target_cube, target=self.target_1 ) ), "name": "drop the cube onto target", "subgoal_segment": f"drop the cube onto target at <>", "choice_label": "drop onto", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_putonto_whenhold( env, planner, target=self.target_1 ), "segment": self.target_1, } ) tasks.append( { "func": (lambda: is_obj_pickup(self, obj=self.target_cube)), "name": f"pick up the cube", "subgoal_segment": f"pick up the cube at <>", "demonstration": True, "failure_func": None, "solve": lambda env, planner: solve_pickup( env, planner, obj=self.target_cube ), "segment": self.target_cube, } ) tasks.append( { "func": ( lambda: is_obj_dropped_onto( self, obj=self.target_cube, target=self.goal_site ) ), "name": "drop the cube onto table", "subgoal_segment": f"drop the cube onto table", "choice_label": "drop onto", "demonstration": True, "failure_func": None, "solve": lambda env, planner: [ solve_putonto_whenhold(env, planner, target=self.goal_site,height=0.01), ], } ) tasks.append( { "func": lambda: static_check( self, timestep=int(self.elapsed_steps), static_steps=20 ), "name": "static", "subgoal_segment": f"static", "demonstration": True, "failure_func": None, "solve": lambda env, planner: [ solve_reset(env, planner), solve_hold_obj(env, planner, static_steps=20), ], }, ) tasks.append( { "func": lambda: static_check( self, timestep=int(self.elapsed_steps), static_steps=60 ), "name": "static", "subgoal_segment": f"static", "specialflag": "swap", "demonstration": True, "failure_func": None, "solve": lambda env, planner: [ solve_hold_obj(env, planner, static_steps=60) ], }, ) tasks.append( { "func": lambda: reset_check(self), "name": "NO RECORD", "subgoal_segment": f"NO RECORD", "demonstration": True, "failure_func": None, "solve": lambda env, planner: [solve_strong_reset(env, planner)], }, ) tasks.append( { "func": (lambda: is_obj_pickup(self, obj=self.target_cube)), "name": f"pick up the cube", "subgoal_segment": f"pick up the cube at <>", "choice_label": "pick up the cube", "demonstration": False, "failure_func": lambda: is_any_obj_pickup(self, self.non_target_cubes), "solve": lambda env, planner: [ solve_pickup(env, planner, obj=self.target_cube) ], "segment": self.target_cube, } ) tasks.append( { "func": ( lambda: is_obj_dropped_onto( self, obj=self.target_cube, target=self.target_target ) ), "name": "place the cube onto the correct target", "subgoal_segment": f"place the cube onto the correct target at <>", "choice_label": "drop onto", "demonstration": False, "failure_func": ( lambda: is_obj_dropped_onto_any( self, obj=self.target_cube, target=self.targets_not_true ) ), "solve": lambda env, planner: [ solve_putonto_whenhold(env, planner, target=self.target_target), ], "segment": self.target_target, } ) self.task_list = tasks self.recovery_pickup_indices, self.recovery_pickup_tasks = task4recovery( self.task_list ) if self.robomme_failure_recovery: self.fail_grasp_task_index = inject_fail_grasp( self.task_list, generator=generator, mode=self.robomme_failure_recovery_mode, ) else: self.fail_grasp_task_index = None except SceneGenerationError: raise except Exception as exc: raise SceneGenerationError( f"Failed to load VideoPlaceButton scene for seed {self.seed}" ) from exc def _initialize_episode(self, env_idx: torch.Tensor, options: dict): with torch.device(self.device): b = len(env_idx) self.table_scene.initialize(env_idx) qpos=reset_panda.get_reset_panda_param("qpos") self.agent.reset(qpos) pose_p=self.goal_site.pose.p.tolist()[0] pose_q=self.goal_site.pose.q.tolist()[0] pose_p[2]=-0.05 self.goal_site.set_pose(sapien.Pose(p=pose_p,q=pose_q)) #print(self.goal_site.pose.p) def _get_obs_extra(self, info: Dict): return dict() def evaluate(self,solve_complete_eval=False): self.successflag=torch.tensor([False]) self.failureflag = torch.tensor([False]) # Use encapsulated sequence task check function if(self.use_demonstrationwrapper==False):# change subgoal after planner ends during recording if solve_complete_eval==True: allow_subgoal_change_this_timestep=True else: allow_subgoal_change_this_timestep=False else:# during demonstration, video needs to call evaluate(solve_complete_eval), video ends and flag changes in demonstrationwrapper if solve_complete_eval==True or self.demonstration_record_traj==False: allow_subgoal_change_this_timestep=True else: allow_subgoal_change_this_timestep=False all_tasks_completed, current_task_name, task_failed,self.current_task_specialflag = sequential_task_check(self, self.task_list,allow_subgoal_change_this_timestep=allow_subgoal_change_this_timestep) # If task failed, mark as failed immediately if task_failed: self.failureflag = torch.tensor([True]) logger.debug(f"Task failed: {current_task_name}") # If static_check succeeds or all tasks completed, set success flag if all_tasks_completed and not task_failed: self.successflag = torch.tensor([True]) return { "success": self.successflag, "fail": self.failureflag, } def compute_dense_reward(self, obs: Any, action: torch.Tensor, info: Dict): tcp_to_obj_dist = torch.linalg.norm( self.agent.tcp_pose.p - self.agent.tcp_pose.p, axis=1 ) reaching_reward = 1 - torch.tanh(5 * tcp_to_obj_dist) reward = reaching_reward*0 return reward def compute_normalized_dense_reward( self, obs: Any, action: torch.Tensor, info: Dict ): return self.compute_dense_reward(obs=obs, action=action, info=info) / 5 #Robomme def step(self, action: Union[None, np.ndarray, torch.Tensor, Dict]): # highlight_obj(self,self.target_cube, start_step=0, end_step=100, cur_step=timestep) if self.current_task_specialflag=="swap": if self.onto_goalsite==False: self.onto_goalsite=True self.start_step=int(self.elapsed_steps.item()) self.end_step=int(self.elapsed_steps.item())+50 if self.swap_target_a is not None and self.swap_target_b is not None: other_bins = self.swap_target_other if self.swap_target_other else None swap_flat_two_lane( self, cube_a=self.swap_target_a, cube_b=self.swap_target_b, start_step=self.start_step, end_step=self.end_step, cur_step=self.elapsed_steps, lane_offset=0.1, smooth=True, keep_upright=True, other_cube=other_bins, ) obs, reward, terminated, truncated, info = super().step(action) return obs, reward, terminated, truncated, info