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.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("ButtonUnmask") class ButtonUnmask(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_hard = { 'bin':15, "pick":2, } config_easy = { 'bin':3, "pick":1, } config_medium = { 'bin':5, "pick":1, } # 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 normalized_robomme_difficulty = normalize_robomme_difficulty( kwargs.pop("difficulty", None) ) 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() ) if normalized_robomme_difficulty is not None: self.difficulty = normalized_robomme_difficulty else: 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" #self.difficulty = "hard" # Use seed to randomly determine number of repetitions (1-5) arbitrarily generator = torch.Generator() generator.manual_seed(seed) self.num_repeats = torch.randint(1, 6, (1,), generator=generator).item() logger.debug(f"Task will repeat {self.num_repeats} times (pickup-drop cycles)") self.generator = generator 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) self.table_scene = TableSceneBuilder( self, robot_init_qpos_noise=self.robot_init_qpos_noise ) self.table_scene.build() button_obb_1 = build_button( self, center_xy=(-0.2, 0), scale=1.5, generator=generator, name="button", randomize=True, randomize_range=(0.1, 0.1) ) # Store first button before building second one self.button_left = self.button self.button_joint_1 = self.button_joint avoid = [button_obb_1] # Generate 3 bins self.spawned_bins = [] for i in range(self.configs[self.difficulty]['bin']): try: bin_actor = spawn_random_bin( self, avoid=avoid, # Use current avoidance list, containing all spawned objects region_center=[0, 0], region_half_size=0.2, min_gap=self.cube_half_size*2, # bins need larger gap, increased to 6x to avoid collision name_prefix=f"bin_{i}", max_trials=256, generator=generator ) except RuntimeError as e: break self.spawned_bins.append(bin_actor) # Assign bin to self.bin_0, self.bin_1 etc. attributes setattr(self, f"bin_{i}", bin_actor) # Add newly generated bin to avoidance list avoid.append(bin_actor) # Generate 3 dynamic cubes under each bin (using fixed position, colors red, green, blue) spawned_dynamic_cubes = [] cube_colors = [(1, 0, 0, 1), (0, 1, 0, 1), (0, 0, 1, 1)] # Red, Green, Blue color_names = ["red", "green", "blue"] # Use seed to randomly shuffle color order shuffle_indices = torch.randperm(len(cube_colors), generator=generator).tolist() cube_colors = [cube_colors[i] for i in shuffle_indices] color_names = [color_names[i] for i in shuffle_indices] # Store color_names for RecordWrapper access self.color_names = color_names # Generate cubes only for first 3 bins for i, bin_actor in enumerate(self.spawned_bins[:3]): # Get bin position bin_pos = bin_actor.pose.p if isinstance(bin_pos, torch.Tensor): bin_pos = bin_pos[0].detach().cpu().numpy() cube_position = [bin_pos[0], bin_pos[1]] # Generate cube using fixed position, colors red, green, blue cube_actor = spawn_fixed_cube( self, position=cube_position, half_size=self.cube_half_size/1.2, color=cube_colors[i], # Use red, green, blue in order name_prefix=f"target_cube_{color_names[i]}", yaw=0.0, # No rotation dynamic=True ) spawned_dynamic_cubes.append(cube_actor) # Assign cube to self.target_cube_red, self.target_cube_green, self.target_cube_blue etc. attributes setattr(self, f"target_cube_{color_names[i]}", cube_actor) # Also store using numeric index for easy access setattr(self, f"target_cube_{i}", cube_actor) # Add newly generated cube to avoidance list avoid.append(cube_actor) tasks = [ { "func": lambda: is_button_pressed(self, obj=self.button_left), "name": "press the button", "subgoal_segment":"press the button at <>", "choice_label": "press the button", "demonstration": False, "failure_func":None, "solve": lambda env, planner: solve_button(env, planner, obj=self.button_left), "segment":self.cap_link, },] tasks.append( { "func": (lambda: is_bin_pickup(self, obj=self.bin_0)), "name": f"pick up the container that hides the {self.color_names[0]} cube", "subgoal_segment":f"pick up the container at <> that hides the {self.color_names[0]} cube", "choice_label": "pick up the container", "demonstration": False, "failure_func": lambda: [ is_any_bin_pickup(self,[bin for bin in self.spawned_bins if bin != self.bin_0]), ], "solve": lambda env, planner: [solve_pickup_bin(env, planner, obj=self.bin_0)], "segment":self.bin_0, }) if self.configs[self.difficulty]['pick']>1: tasks.append({ "func": (lambda: is_bin_putdown(self, obj=self.bin_0)), "name": "put down the container", "subgoal_segment":"put down the container", "choice_label": "put down the container", "demonstration": False, "failure_func": lambda:is_any_bin_pickup(self,[bin for bin in self.spawned_bins if bin != self.bin_0]), "solve": lambda env, planner: solve_putdown_whenhold(env, planner), }) tasks.append( { "func": (lambda: is_bin_pickup(self, obj=self.bin_1)), "name": f"pick up the container that hides the {self.color_names[1]} cube", "subgoal_segment":f"pick up the container at <> that hides the {self.color_names[1]} cube", "choice_label": "pick up the container", "demonstration": False, "failure_func": lambda: is_any_bin_pickup(self,[bin for bin in self.spawned_bins if bin != self.bin_1]), "solve": lambda env, planner: solve_pickup_bin(env, planner, obj=self.bin_1), "segment":self.bin_1, }) self.task_list = tasks # Set recovery related attributes # Record pickup related task indices and items for recovery self.recovery_pickup_indices, self.recovery_pickup_tasks = task4recovery(self.task_list) if self.robomme_failure_recovery: # Only inject an intentional failed grasp when recovery mode is enabled self.fail_grasp_task_index = inject_fail_grasp( self.task_list, generator=self.generator, mode=self.robomme_failure_recovery_mode, ) else: self.fail_grasp_task_index = None 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) 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) #print(f"Current Task: {current_task_name}") # 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 def _get_other_bins_for_pair(self, idx_a: int, idx_b: int): """Return bins that are not part of the provided pair indices.""" if not hasattr(self, "spawned_bins"): return [] total_bins = len(self.spawned_bins) if idx_a >= total_bins or idx_b >= total_bins: return [] # Prefer precomputed lists when available if hasattr(self, "otherbins") and idx_a < len(self.otherbins): other_candidates = [ bin_actor for bin_actor in self.otherbins[idx_a] if bin_actor is not self.spawned_bins[idx_b] ] return other_candidates return [ bin_actor for i, bin_actor in enumerate(self.spawned_bins) if i not in (idx_a, idx_b) ] #Robomme def step(self, action: Union[None, np.ndarray, torch.Tensor, Dict]): timestep = self.elapsed_steps #Lift and drop bins (bin_0 to bin_4 if they exist) for i in range(15): bin_attr = f"bin_{i}" if hasattr(self, bin_attr): lift_and_drop_objects_back_to_original( self, obj=getattr(self, bin_attr), start_step=0, end_step=32*2, cur_step=timestep, ) obs, reward, terminated, truncated, info = super().step(action) return obs, reward, terminated, truncated, info