import argparse import os import time from dataclasses import dataclass from datetime import datetime from typing import List, Tuple import mujoco import numpy as np from tqdm import tqdm from dataset import TrajectoryBuffer @dataclass class Config: """Configuration for the multi-ball-in-bottle environment.""" num_balls: int = 4 bottle_length: float = 0.10 # x dimension (m) bottle_width: float = 0.10 # y dimension (m) bottle_height: float = 0.50 # z dimension (m) wall_thickness: float = 0.005 ball_radius: float = 0.01 ball_mass: float = 0.003 timestep: float = 0.001 # Initialization ranges min_z: float = 0.4 max_z: float = 0.5 max_xy_speed: float = 0.30 # m/s # Rendering render_width: int = 640 render_height: int = 480 class BottleMultiBallEnv: """Four free-falling balls inside an open-top square bottle.""" def __init__(self, headless: bool = True, config: Config | None = None, seed: int = 0): self.config = config or Config() self.headless = headless self.rng = np.random.RandomState(seed) self.model = mujoco.MjModel.from_xml_string(self._build_xml()) self.data = mujoco.MjData(self.model) self.viewer = None self.renderer = None self.cam_id = mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_CAMERA, "bottle_cam") if not self.headless: try: import mujoco.viewer as mj_viewer # type: ignore self.viewer = mj_viewer.launch_passive(self.model, self.data) except Exception: # Fallback to offscreen renderer; requires a valid GL context to show frames self.renderer = mujoco.Renderer( self.model, width=self.config.render_width, height=self.config.render_height ) # Cache indices for qpos/qvel blocks of each ball self.ball_qpos_idx = [] self.ball_qvel_idx = [] for i in range(1, self.config.num_balls + 1): name = f"ball{i}_free" self.ball_qpos_idx.append( self.model.jnt_qposadr[mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_JOINT, name)] ) self.ball_qvel_idx.append( self.model.jnt_dofadr[mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_JOINT, name)] ) def _build_xml(self) -> str: c = self.config half_len = c.bottle_length / 2 half_wid = c.bottle_width / 2 t = c.wall_thickness h = c.bottle_height ball_rad = c.ball_radius return f""" """ def _ball_body_xml(self, idx: int, radius: float) -> str: return f""" """ def reset(self) -> Tuple[np.ndarray, bool]: mujoco.mj_resetData(self.model, self.data) positions = self._sample_initial_positions() velocities = self._sample_initial_velocities() for i in range(self.config.num_balls): qpos_idx = self.ball_qpos_idx[i] qvel_idx = self.ball_qvel_idx[i] pos = positions[i] vel = velocities[i] self.data.qpos[qpos_idx : qpos_idx + 3] = pos # quaternion wxyz = [1, 0, 0, 0] self.data.qpos[qpos_idx + 3 : qpos_idx + 7] = np.array([1.0, 0.0, 0.0, 0.0]) self.data.qvel[qvel_idx : qvel_idx + 3] = vel self.data.qvel[qvel_idx + 3 : qvel_idx + 6] = np.zeros(3) mujoco.mj_forward(self.model, self.data) return self.get_obs() def _sample_initial_positions(self) -> np.ndarray: c = self.config positions = [] margin = c.ball_radius * 2.5 tries = 0 while len(positions) < c.num_balls: x = self.rng.uniform(-c.bottle_length / 2 + margin, c.bottle_length / 2 - margin) y = self.rng.uniform(-c.bottle_width / 2 + margin, c.bottle_width / 2 - margin) z = self.rng.uniform(c.min_z, c.max_z) candidate = np.array([x, y, z], dtype=np.float64) if all(np.linalg.norm(candidate[:2] - p[:2]) > c.ball_radius * 2 for p in positions): positions.append(candidate) tries += 1 if tries > 1000: # fallback to avoid infinite loop positions.append(candidate) return np.stack(positions, axis=0) def _sample_initial_velocities(self) -> np.ndarray: c = self.config speeds = self.rng.uniform(0.0, c.max_xy_speed, size=(c.num_balls,)) angles = self.rng.uniform(0.0, 2 * np.pi, size=(c.num_balls,)) vx = speeds * np.cos(angles) vy = speeds * np.sin(angles) vz = np.zeros_like(vx) return np.stack([vx, vy, vz], axis=1) def step(self): mujoco.mj_step(self.model, self.data) return self.get_obs() def get_obs(self) -> Tuple[np.ndarray, bool]: # Collect positions and velocities for four balls pos_list: List[np.ndarray] = [] vel_list: List[np.ndarray] = [] for i in range(self.config.num_balls): qpos_idx = self.ball_qpos_idx[i] qvel_idx = self.ball_qvel_idx[i] pos_list.append(self.data.qpos[qpos_idx : qpos_idx + 3].copy()) vel_list.append(self.data.qvel[qvel_idx : qvel_idx + 3].copy()) positions = np.stack(pos_list, axis=0) velocities = np.stack(vel_list, axis=0) timestamp = np.array([self.data.time], dtype=np.float32) obs = np.concatenate([positions.reshape(-1), velocities.reshape(-1), timestamp]).astype(np.float32) done = self._check_done(positions) return obs, done def _check_done(self, positions: np.ndarray) -> bool: c = self.config half_len = c.bottle_length / 2 half_wid = c.bottle_width / 2 if np.any(positions[:, 2] < 0.0): return True if np.any(np.abs(positions[:, 0]) > half_len) or np.any(np.abs(positions[:, 1]) > half_wid): return True return False def render(self): if self.viewer is not None: self.viewer.sync() return None if self.renderer is not None: self.renderer.update_scene(self.data, camera=self.cam_id) return self.renderer.render() return None def close(self): if self.viewer is not None: try: self.viewer.close() except Exception: pass if self.renderer is not None: self.renderer.free() def collect_multi_ball_data( env: BottleMultiBallEnv, target_trajectories: int, steps_per_traj: int, render: bool = False, realtime: bool = False, ) -> TrajectoryBuffer: record_stride = 5 # record once every 5 env steps buffer = TrajectoryBuffer(steps_per_traj) pbar = tqdm(total=target_trajectories, desc="Collecting multi-ball data") for _ in range(target_trajectories): obs, done = env.reset() recorded = 0 for step_idx in range(steps_per_traj * record_stride): if step_idx % record_stride == 0: obs_np = obs[None, :] ext_obs_np = obs_np action_np = np.zeros((1, 1), dtype=np.float32) # placeholder action reward_np = np.zeros((1,), dtype=np.float32) done_np = np.array([done], dtype=np.bool_) buffer.append_step(obs_np, ext_obs_np, action_np, reward_np, done_np) recorded += 1 if recorded >= steps_per_traj: break obs, done = env.step() if render: env.render() if realtime: time.sleep(env.model.opt.timestep) if done: break pbar.update(1) pbar.close() return buffer def parse_args(): parser = argparse.ArgumentParser(description="Collect multi-ball contact-rich dataset inside a bottle.") parser.add_argument("--trajectories", type=int, default=1024, help="Number of trajectories to collect") parser.add_argument("--steps_per_trajectory", type=int, default=8192, help="Steps per trajectory") parser.add_argument("--out_dir", type=str, default="./dataset/multi_bb/", help="Output directory") parser.add_argument("--seed", type=int, default=42, help="Random seed") parser.add_argument("--headless", action="store_true", help="Disable on-screen rendering") parser.add_argument("--realtime", action="store_true", help="Sleep to match real time") parser.add_argument("--demo", action="store_true", help="Run a short demo instead of full collection") parser.add_argument("--num_balls", type=int, default=2, help="Number of balls in the bottle") return parser.parse_args() def main(): args = parse_args() np.random.seed(args.seed) config = Config(num_balls=args.num_balls) env = BottleMultiBallEnv(headless=args.headless, config=config, seed=args.seed) try: if args.demo: obs, _ = env.reset() print(f"Initial obs shape: {obs.shape}") for _ in range(500): obs, done = env.step() if not args.headless: env.render() if args.realtime: time.sleep(env.model.opt.timestep) if done: obs, _ = env.reset() else: os.makedirs(args.out_dir, exist_ok=True) buffer = collect_multi_ball_data( env=env, target_trajectories=args.trajectories, steps_per_traj=args.steps_per_trajectory, render=not args.headless, realtime=args.realtime, ) timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") file_stem = f"multi_bb_{timestamp}" dataset_path = os.path.join(args.out_dir, f"{file_stem}.npz") buffer.save(dataset_path) meta = { "environment": "multi_ball_bottle", "trajectories": args.trajectories, "steps_per_trajectory": args.steps_per_trajectory, "total_trajectories": len(buffer), "total_steps": len(buffer) * args.steps_per_trajectory, "seed": args.seed, "config": config.__dict__, "timestamp": timestamp, "headless": args.headless, } import pickle metadata_path = os.path.join(args.out_dir, f"{file_stem}_metadata.pkl") with open(metadata_path, "wb") as f: pickle.dump(meta, f) print(f"[INFO] Dataset saved: {dataset_path}") print(f"[INFO] Metadata saved: {metadata_path}") print(f"[INFO] Collected {len(buffer)} trajectories") finally: env.close() if __name__ == "__main__": main()