446 lines
18 KiB
Python
446 lines
18 KiB
Python
"""
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ml/env.py - Gymnasium Environment for JackBot Hexapod RL Training
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"""
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import time
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import math
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from typing import Optional, Tuple, Dict, Any, List
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import gymnasium as gym
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from gymnasium import spaces
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import numpy as np
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from config import cfg
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from Robot import Robot, PyBulletBackend
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from ml.SimManager import SimManager
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from ml.MetricsOverlay import MetricsHUD, LeaderCrown
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# Color Palette RGBA for Terminated/Failed Robots
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COLOR_FAILED = [0.3, 0.3, 0.3, 0.6] # Collapsed / Tilted Robot (Dark Semi-Transparent Gray)
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class JackBotEnv(gym.Env):
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"""Gymnasium environment wrapping JackBot hexapods with floating text & crown overlay."""
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def __init__(
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self,
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use_gui: bool = True,
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random_command: bool = True,
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robot_spacing: float = 0.5,
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start_pose: str = "init_deg",
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max_episode_steps: int = 5000,
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urdf_path: str = cfg.urdf_path,
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):
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super().__init__()
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self.use_gui = use_gui
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self.random_command = random_command
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self.robot_spacing = robot_spacing
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self.start_pose = start_pose
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self.max_episode_steps = max_episode_steps
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self.urdf_path = urdf_path
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self.episode_count = 0
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self.step_count = 0
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self.total_steps = 0
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self.cumulative_reward = 0.0
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self.robot_rewards = [0.0]
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self.failed_robots_mask = [False]
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self._first_reset = True
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# Initialize Simulation Manager
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self.sim_manager = SimManager(use_gui=self.use_gui)
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self.sim_manager.connect()
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# Connect physics world
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self.plane, self.pb_robots, self.robot_joint_indices = self.sim_manager.load_scene(
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self.urdf_path, self.robot_spacing, self._robot_base_position
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)
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# Instantiate Robot Python wrappers per PyBullet body ID
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self.robots = [
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Robot(
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backend_type=PyBulletBackend(self.sim_manager, body_id=pb_id),
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start_pose=self.start_pose,
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urdf_path=self.urdf_path
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)
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for pb_id in self.pb_robots
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]
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# Action (18 joint deltas per robot) & Observation (18 angles + 4 command dims per robot)
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action_dim = 18
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obs_dim = 18 + 4
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self.action_space = spaces.Box(-1.0, 1.0, shape=(action_dim,), dtype=np.float32)
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self.observation_space = spaces.Box(-np.inf, np.inf, shape=(obs_dim,), dtype=np.float32)
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self.commands = np.zeros((1, 4), dtype=np.float32)
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self.last_action = np.zeros(action_dim, dtype=np.float32)
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self.target_height = 0.14
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self.collapse_height_fraction = 0.55
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self.tilt_failure_rad = 0.9
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# Small random exploration pulse settings
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self.exploration_bonus_prob = 0.03
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self.exploration_bonus_interval = 120
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self.exploration_bonus_scale = 0.08
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self.exploration_bonus_active = False
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self.start_positions = [[0.0, 0.0, 0.0]]
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self.max_distance_from_start = [0.0]
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self.max_survival_steps = 0
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self.curriculum_phase = 0
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self.curriculum_episode_limit = 150
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self.curriculum_stage_requirements = {
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1: {"survival_steps": 1000, "distance": 0.00, "stability_roll_pitch": 0.35},
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2: {"survival_steps": 350, "distance": 10.0, "stability_roll_pitch": 0.30},
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3: {"survival_steps": 550, "distance": 15.00, "stability_roll_pitch": 0.25},
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}
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# Floating HUD & Leader Crown Visualizers
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self.hud = MetricsHUD(physics_client_id=self.sim_manager.physics_client)
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self.leader_crown = LeaderCrown(physics_client_id=self.sim_manager.physics_client)
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self.last_time = time.time()
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def _robot_base_position(self, robot_id: int, spacing: float = 0.5) -> list[float]:
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return [0.0, 0.0, 0.14]
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def sample_command(self) -> np.ndarray:
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"""Curriculum command sampler with survival-gated difficulty progression."""
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phase = self.curriculum_phase
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if phase == 0:
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# Phase 1: Forward Walking Focus
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vx = np.random.uniform(0.5, 1.0)
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vy = 0.0
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vz = 0.0
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omega = 0.0
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elif phase == 1:
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# Phase 2: Forward/Backward + Turning
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vx = np.random.uniform(-1.0, 1.0)
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vy = 0.0
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vz = 0.0
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omega = np.random.uniform(-0.8, 0.8)
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else:
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# Phase 3: Full Omnidirectional Movement
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vx = np.random.uniform(-1.0, 1.0)
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vy = np.random.uniform(-0.5, 0.5)
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vz = 0.0
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omega = np.random.uniform(-1.0, 1.0)
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return np.array([vx, vy, vz, omega], dtype=np.float32)
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def reset(self, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None):
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super().reset(seed=seed)
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self.episode_count += 1
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self.step_count = 0
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self.cumulative_reward = 0.0
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self.robot_rewards = [0.0]
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self.failed_robots_mask = [False]
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for idx, (pb_id, robot_obj) in enumerate(zip(self.pb_robots, self.robots)):
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spawn_pos = self._robot_base_position(idx, self.robot_spacing)
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spawn_orn = [0.0, 0.0, 0.0, 1.0]
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self.sim_manager.reset_robot_base(pb_id, spawn_pos, spawn_orn)
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robot_obj.reset_to_init()
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if self.use_gui:
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self.sim_manager.set_robot_color(pb_id, [1.0, 1.0, 1.0, 1.0])
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self.last_action = np.zeros(self.action_space.shape[0], dtype=np.float32)
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self.max_distance_from_start = [0.0]
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self.max_survival_steps = 0
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self.exploration_bonus_active = False
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if self._first_reset:
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self.curriculum_phase = 0
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self.curriculum_episode_limit = min(self.max_episode_steps, 150)
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self._first_reset = False
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if self.random_command:
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self.commands = np.stack([self.sample_command() for _ in range(1)])
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else:
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self.commands = np.zeros((1, 4), dtype=np.float32)
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for idx, pb_id in enumerate(self.pb_robots):
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pos, _ = self.sim_manager.get_robot_pose(pb_id)
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self.start_positions[idx] = [float(pos[0]), float(pos[1]), float(pos[2])]
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# Settle for 200 steps after dropping in a standing position, then measure target height
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self.target_height = self.sim_manager.settle_and_measure_height(
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self.pb_robots, steps=200, fallback_height=0.14
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)
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if self.use_gui:
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self.hud.reset()
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self.leader_crown.reset()
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self._update_hud()
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return self._get_obs(), {}
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def get_robot_velocities(self) -> list:
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"""Exposes velocities for the SB3 metrics callback."""
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vels = []
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for pb_id in self.pb_robots:
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lin_v, ang_v = self.sim_manager.get_robot_velocity(pb_id)
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vels.append((lin_v, ang_v))
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return vels
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def get_robot_distance_metrics(self) -> list:
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"""Exposes distance-from-start metrics for logging without affecting reward."""
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metrics = []
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for idx, pb_id in enumerate(self.pb_robots):
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pos, _ = self.sim_manager.get_robot_pose(pb_id)
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start_x, start_y, _ = self.start_positions[idx]
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dist = float(np.linalg.norm(np.array([pos[0] - start_x, pos[1] - start_y], dtype=np.float32)))
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self.max_distance_from_start[idx] = max(self.max_distance_from_start[idx], dist)
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metrics.append((dist, self.max_distance_from_start[idx]))
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self.max_survival_steps = max(self.max_survival_steps, self.step_count)
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return metrics
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def get_current_robot_metrics(self) -> list:
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"""Returns current per-robot reward, distance, and velocity summaries for alive robots only."""
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metrics = []
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for idx, pb_id in enumerate(self.pb_robots):
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if self.failed_robots_mask[idx]:
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continue
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pos, _ = self.sim_manager.get_robot_pose(pb_id)
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linear_vel, angular_vel = self.sim_manager.get_robot_velocity(pb_id)
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start_x, start_y, _ = self.start_positions[idx]
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dist = float(np.linalg.norm(np.array([pos[0] - start_x, pos[1] - start_y], dtype=np.float32)))
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speed = float(np.linalg.norm(np.array([linear_vel[0], linear_vel[1]], dtype=np.float32)))
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yaw_rate = float(abs(angular_vel[2]))
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metrics.append({
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"reward": float(self.robot_rewards[idx]),
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"distance_from_start": dist,
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"speed": speed,
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"yaw_rate": yaw_rate,
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"alive": True,
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"survival_steps": int(self.step_count),
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})
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return metrics
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def get_survival_steps(self) -> int:
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"""Returns the current survival length for the environment's current episode."""
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return int(self.step_count)
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def _get_obs(self) -> np.ndarray:
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obs_list = []
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for idx, (pb_id, joint_indices) in enumerate(zip(self.pb_robots, self.robot_joint_indices)):
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joint_angles = self.sim_manager.get_robot_joint_angles(pb_id, joint_indices)
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robot_obs = np.concatenate([joint_angles, self.commands[idx]])
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obs_list.append(robot_obs)
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return np.concatenate(obs_list).astype(np.float32)
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def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]:
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self.step_count += 1
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self.total_steps += 1
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previous_action = self.last_action.copy()
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self.last_action = action.copy()
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self._update_curriculum()
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# Resample commands every 300 steps during long episodes
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if self.random_command and (self.step_count % 300 == 0 or self._curriculum_advanced):
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self.commands = np.stack([self.sample_command() for _ in range(1)])
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action_per_robot = action.reshape(1, 18)
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for robot, act in zip(self.robots, action_per_robot):
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robot.apply_rl_action(act)
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self.sim_manager.step()
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self._update_robot_failures()
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self.get_robot_distance_metrics()
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obs = self._get_obs()
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reward, per_robot_step_rewards = self._compute_reward(action, previous_action)
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self.cumulative_reward += reward
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for idx, r_step in enumerate(per_robot_step_rewards):
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self.robot_rewards[idx] += r_step
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terminated = self._is_done()
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truncated = self.step_count >= self.curriculum_episode_limit
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self._update_hud()
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self._update_leader_visuals()
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return obs, reward, terminated, truncated, {}
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def _phase_progress_ready(self, phase: int) -> bool:
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if phase not in self.curriculum_stage_requirements:
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return False
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if not self.pb_robots or not self.max_distance_from_start:
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return False
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req = self.curriculum_stage_requirements[phase]
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survival_ok = self.max_survival_steps >= req["survival_steps"]
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distance_ok = self.max_distance_from_start[0] >= req["distance"]
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position, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robots[0])
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stability_ok = abs(roll) <= req["stability_roll_pitch"] and abs(pitch) <= req["stability_roll_pitch"]
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height_ok = position[2] >= max(0.09, self.target_height * 0.85)
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return survival_ok and distance_ok and stability_ok and height_ok
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def _update_curriculum(self):
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self._curriculum_advanced = False
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phase_labels = {
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0: "stand-and-forward",
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1: "turn-and-direction",
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2: "omni-direction",
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3: "full-command",
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}
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if self.curriculum_phase < 1 and self._phase_progress_ready(1):
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self.curriculum_phase = 1
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self.curriculum_episode_limit = min(self.max_episode_steps, 400)
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self._curriculum_advanced = True
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print(f"[Curriculum] phase {self.curriculum_phase} ({phase_labels.get(self.curriculum_phase, 'unknown')}) unlocked at total step {self.total_steps}")
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elif self.curriculum_phase < 2 and self._phase_progress_ready(2):
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self.curriculum_phase = 2
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self.curriculum_episode_limit = min(self.max_episode_steps, 700)
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self._curriculum_advanced = True
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print(f"[Curriculum] phase {self.curriculum_phase} ({phase_labels.get(self.curriculum_phase, 'unknown')}) unlocked at total step {self.total_steps}")
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elif self.curriculum_phase < 3 and self._phase_progress_ready(3):
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self.curriculum_phase = 3
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self.curriculum_episode_limit = min(self.max_episode_steps, self.max_episode_steps)
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self._curriculum_advanced = True
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print(f"[Curriculum] phase {self.curriculum_phase} ({phase_labels.get(self.curriculum_phase, 'unknown')}) unlocked at total step {self.total_steps}")
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def _compute_reward(self, action: np.ndarray, previous_action: np.ndarray) -> Tuple[float, list[float]]:
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rewards = []
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current_actions = action.reshape(1, 18)
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previous_actions = previous_action.reshape(1, 18)
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for idx, pb_id in enumerate(self.pb_robots):
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pos, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(pb_id)
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linear_vel, angular_vel = self.sim_manager.get_robot_velocity(pb_id)
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command = self.commands[idx]
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cmd_vx = command[0]
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cmd_vy = command[1]
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cmd_yaw = command[3]
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# 1. LINEAR VECTOR SPEED MAXIMIZATION
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cmd_dir = np.array([cmd_vx, cmd_vy], dtype=np.float32)
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cmd_norm = np.linalg.norm(cmd_dir)
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if cmd_norm > 0.05:
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unit_cmd_dir = cmd_dir / cmd_norm
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actual_vel_2d = np.array([linear_vel[0], linear_vel[1]], dtype=np.float32)
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aligned_speed = float(np.dot(actual_vel_2d, unit_cmd_dir))
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linear_speed_reward = 2.5 * aligned_speed
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perp_vel = actual_vel_2d - aligned_speed * unit_cmd_dir
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drift_penalty = 0.35 * float(np.dot(perp_vel, perp_vel))
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else:
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linear_speed_reward = 0.0
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drift_penalty = 1.0 * (linear_vel[0]**2 + linear_vel[1]**2)
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# 2. TURNING SPEED MAXIMIZATION
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actual_yaw_rate = angular_vel[2]
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if abs(cmd_yaw) > 0.05:
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turning_reward = 1.5 * (actual_yaw_rate * cmd_yaw)
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else:
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turning_reward = -0.6 * (actual_yaw_rate ** 2)
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# 3. SMALL ALIVE BONUS + AGE-RAMPED STILLNESS PENALTY
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alive_reward = 0.5
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age_ratio = min(1.0, self.step_count / max(1, self.curriculum_episode_limit))
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still_penalty = 0.0
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if (cmd_norm > 0.1 or abs(cmd_yaw) > 0.1) and (abs(linear_vel[0]) < 0.02 and abs(actual_yaw_rate) < 0.05):
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still_penalty = 0.35 + 0.85 * age_ratio
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# 4. POSTURE & STABILITY PENALTIES
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height_penalty = 10.0 * ((self.target_height - pos[2]) ** 2) if pos[2] < self.target_height else 0.0
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stability_penalty = 1.5 * (roll**2 + pitch**2)
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print(-height_penalty)
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control_delta = np.abs(current_actions[idx] - previous_actions[idx])
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large_delta_mask = control_delta > 0.12
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large_delta_penalty = 0.002 * float(np.sum(np.square(control_delta[large_delta_mask]))) if np.any(large_delta_mask) else 0.0
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r_step = (
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alive_reward
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+ linear_speed_reward
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+ turning_reward
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- drift_penalty
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- still_penalty
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- height_penalty
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- stability_penalty
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- large_delta_penalty
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)
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rewards.append(r_step)
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return float(np.sum(rewards)), rewards
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def _is_done(self) -> bool:
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"""Returns True when the robot has entered a failed state."""
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return bool(self.failed_robots_mask[0]) if self.failed_robots_mask else False
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def _update_hud(self):
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if not self.use_gui or not self.pb_robots:
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return
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now = time.time()
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fps = 1.0 / max(now - self.last_time, 1e-5)
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self.last_time = now
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heights = []
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rolls = []
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pitches = []
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for pb_id in self.pb_robots:
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pos, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(pb_id)
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heights.append(pos[2])
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rolls.append(math.degrees(roll))
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pitches.append(math.degrees(pitch))
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avg_height = float(np.mean(heights))
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avg_roll_pitch = (float(np.mean(rolls)), float(np.mean(pitches)))
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self.hud.update(
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episode=self.episode_count,
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step=self.total_steps,
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robot_rewards=self.robot_rewards,
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cmd_vel=self.commands[0],
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fps=fps,
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avg_height=avg_height,
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roll_pitch=avg_roll_pitch
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)
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def _update_leader_visuals(self):
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if not self.use_gui:
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return
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best_idx = int(np.argmax(self.robot_rewards))
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leader_pb_id = self.pb_robots[best_idx]
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leader_pos, _ = self.sim_manager.get_robot_pose(leader_pb_id)
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self.leader_crown.update(leader_pos)
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def _update_robot_failures(self):
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"""Checks failure condition and colors failed robots dark gray."""
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for idx, pb_id in enumerate(self.pb_robots):
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if self.failed_robots_mask[idx]:
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continue
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|
|
|
position, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(pb_id)
|
|
|
|
collapse_threshold = max(0.06, self.collapse_height_fraction * self.target_height)
|
|
is_tilted = abs(roll) > self.tilt_failure_rad or abs(pitch) > self.tilt_failure_rad
|
|
is_collapsed = position[2] < collapse_threshold
|
|
|
|
if is_tilted or is_collapsed:
|
|
self.failed_robots_mask[idx] = True
|
|
if self.use_gui:
|
|
self.sim_manager.set_robot_color(pb_id, COLOR_FAILED)
|
|
|
|
def close(self):
|
|
self.sim_manager.disconnect() |