code reduction for training and eval
reward and everything else changed again wont be the last time
This commit is contained in:
@@ -9,14 +9,15 @@ 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 stable_baselines3.common.callbacks import BaseCallback
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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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from ml.MetricsOverlay import MetricsHUD
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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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COLOR_FAILED = [0.3, 0.3, 0.3, 0.6]
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class CurriculumPhase(IntEnum):
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@@ -28,93 +29,92 @@ class CurriculumPhase(IntEnum):
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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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"""Gymnasium environment wrapping a single JackBot hexapod."""
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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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max_episode_steps: int = 3000,
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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.max_robot_speed = 0.8
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self.max_robot_speed = 0.6
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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.consecutive_still_steps = [0]
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self.failed_robots_mask = [False]
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self.robot_reward = 0.0
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self.consecutive_still_steps = 0
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self.is_failed = False
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self.episode_count = 0
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self.episode_height_sum = 0.0
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self.episode_roll_sum = 0.0
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self.episode_pitch_sum = 0.0
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self._curriculum_advanced = False
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self._first_reset = True
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# Dynamic Command Resampling Timing (60 Hz control loop)
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self.control_freq = 60
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self.min_cmd_hold_steps = int(2.0 * self.control_freq) # 120 steps (2s)
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self.max_cmd_hold_steps = int(6.0 * self.control_freq) # 360 steps (6s)
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self.next_cmd_resample_step = 0
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self.initial_stand_steps = 120 # Mandatory 2s standing window at episode reset
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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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# Connect physics world & load single robot
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self.plane, pb_robots, robot_joint_indices = self.sim_manager.load_scene(
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self.urdf_path, 0.0, self._robot_base_position
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)
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self.pb_robot = pb_robots[0]
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self.joint_indices = robot_joint_indices[0]
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# Instantiate Robot Python wrapper (start_pose is managed inside Robot.py)
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self.robot = Robot(
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backend_type=PyBulletBackend(self.sim_manager, body_id=self.pb_robot),
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urdf_path=self.urdf_path
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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 (18 joint deltas) & Observation (18 angles + 4 command dims)
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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.command = np.zeros(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.target_height = 0.122
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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.foot_link_indices = self._find_foot_link_indices()
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self.start_position = [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.default_joint_angles = np.zeros(18, dtype=np.float32)
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# Curriculum Initialization via Enum
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self.curriculum_phase = CurriculumPhase.STAND_ONLY
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self.curriculum_episode_limit = 300 # 300 steps limit gives headroom for 400-step requirement
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# Gates required to unlock each target phase
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self.curriculum_stage_requirements = {
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CurriculumPhase.FORWARD: {
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"survival_steps": 400, # Must survive ~8 seconds
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"min_avg_height_ratio": 0.90, # Average height >= 90% of target
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"stability_roll_pitch": 0.18, # Max ~10 degrees tilt
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"survival_steps": 300,
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"min_avg_height_ratio": 0.88,
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"max_avg_roll_pitch": 0.18, # ~10 degrees average
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},
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CurriculumPhase.TURN_AND_DIRECTION: {
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"survival_steps": 500,
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"min_forward_distance": 2.5, # Must walk +2.5m forward (+X)
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"max_lateral_drift": 0.8, # Max 0.8m drift on Y axis
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"min_forward_distance": 2.5,
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"max_lateral_drift": 0.8,
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"min_avg_height_ratio": 0.85,
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"stability_roll_pitch": 0.25,
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},
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@@ -132,51 +132,42 @@ class JackBotEnv(gym.Env):
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},
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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 _robot_base_position(self, robot_id: int, spacing: float = 0.0) -> list[float]:
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return [0.0, 0.0, 0.13]
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def _find_foot_link_indices(self) -> list:
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return self.sim_manager.get_foot_link_indices(self.pb_robot)
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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 == CurriculumPhase.STAND_ONLY:
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# Phase 0: Pure Standing (Zero commands)
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vx = 0.0
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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 == CurriculumPhase.FORWARD:
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# Phase 1: Straight Forward Walking + Standing Checks
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if np.random.random() < 0.30:
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# 30% chance: Stand Still Command (vx = 0)
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vx = 0.0
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else:
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# 70% chance: Forward Walk Command
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vx = np.random.uniform(0.15, 0.50)
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vy, vz, omega = 0.0, 0.0, 0.0
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stand_probabilities = {
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CurriculumPhase.STAND_ONLY: 1.0,
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CurriculumPhase.FORWARD: 0.25,
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CurriculumPhase.TURN_AND_DIRECTION: 0.20,
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CurriculumPhase.OMNI_DIRECTION: 0.15,
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CurriculumPhase.FULL_COMMAND: 0.15,
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}
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if np.random.random() < stand_probabilities.get(phase, 0.15):
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return np.zeros(4, dtype=np.float32)
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if phase == CurriculumPhase.FORWARD:
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vx = np.random.uniform(0.15, 0.50)
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vy, vz, omega = 0.0, 0.0, 0.0
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elif phase == CurriculumPhase.TURN_AND_DIRECTION:
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# Phase 2: Forward/Backward + Turning + Standing
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if np.random.random() < 0.20:
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vx, omega = 0.0, 0.0
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else:
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vx = np.random.uniform(-1.0, 1.0)
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omega = np.random.uniform(-0.8, 0.8)
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vx = np.random.uniform(-0.8, 0.8)
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vy, vz = 0.0, 0.0
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omega = np.random.uniform(-0.8, 0.8)
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elif phase == CurriculumPhase.OMNI_DIRECTION:
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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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vx = np.random.uniform(-0.8, 0.8)
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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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omega = np.random.uniform(-0.8, 0.8)
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else:
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# Phase 4: Full Unconstrained Commands
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vx = np.random.uniform(-1.0, 1.0)
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vy = np.random.uniform(-1.0, 1.0)
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vz = 0.0
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@@ -189,177 +180,158 @@ class JackBotEnv(gym.Env):
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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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self.robot_reward = 0.0
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self.is_failed = False
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self.episode_height_sum = 0.0
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self.episode_roll_sum = 0.0
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self.episode_pitch_sum = 0.0
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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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spawn_pos = self._robot_base_position(0)
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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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self.sim_manager.reset_robot_base(self.pb_robot, spawn_pos, spawn_orn)
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self.robot.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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if self.use_gui:
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self.sim_manager.set_robot_color(self.pb_robot, [1.0, 1.0, 1.0, 1.0])
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self.consecutive_still_steps = [0 for _ in range(len(self.pb_robots))]
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self.consecutive_still_steps = 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_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 = CurriculumPhase.STAND_ONLY
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self.curriculum_episode_limit = min(self.max_episode_steps, 500)
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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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self.command = np.zeros(4, dtype=np.float32)
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self.next_cmd_resample_step = self.initial_stand_steps
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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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pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
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self.start_position = [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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[self.pb_robot], steps=200, fallback_height=0.122
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)
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self.default_joint_angles = np.array(
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self.sim_manager.get_robot_joint_angles(self.pb_robot, self.joint_indices),
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dtype=np.float32
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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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if idx == 0:
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self.episode_height_sum += float(pos[2])
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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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"""Returns metric summary for the callback."""
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if self.is_failed:
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return []
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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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pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
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linear_vel, angular_vel = self.sim_manager.get_robot_velocity(self.pb_robot)
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start_x, start_y, _ = self.start_position
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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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return [{
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"reward": float(self.robot_reward),
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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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"phase_name": self.curriculum_phase.name,
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}]
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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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joint_angles = self.sim_manager.get_robot_joint_angles(self.pb_robot, self.joint_indices)
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return np.concatenate([joint_angles, self.command]).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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if self.random_command and (self.step_count >= self.next_cmd_resample_step or self._curriculum_advanced):
|
||||
self.command = self.sample_command()
|
||||
random_interval = np.random.randint(self.min_cmd_hold_steps, self.max_cmd_hold_steps + 1)
|
||||
self.next_cmd_resample_step = self.step_count + random_interval
|
||||
|
||||
action_per_robot = action.reshape(1, 18)
|
||||
|
||||
for robot, act in zip(self.robots, action_per_robot):
|
||||
robot.apply_rl_action(act)
|
||||
self.robot.apply_rl_action(action)
|
||||
|
||||
if self.step_count % 60 == 0:
|
||||
random_force = np.random.uniform(-2.0, 2.0, size=2) # X and Y push (Newtons)
|
||||
random_force = np.random.uniform(-2.0, 2.0, size=2)
|
||||
self.sim_manager.apply_external_force(
|
||||
body_id=self.pb_robots[0],
|
||||
body_id=self.pb_robot,
|
||||
force=[random_force[0], random_force[1], 0.0]
|
||||
)
|
||||
|
||||
render_freq = 10 # Only draw 1 in every 10 frames
|
||||
|
||||
if self.use_gui and self.step_count % render_freq != 0:
|
||||
self.sim_manager.set_rendering(False)
|
||||
|
||||
self.sim_manager.step()
|
||||
|
||||
if self.use_gui and self.step_count % render_freq == 0:
|
||||
self.sim_manager.set_rendering(True)
|
||||
|
||||
self._update_robot_failures()
|
||||
self.get_robot_distance_metrics()
|
||||
self._update_robot_failure()
|
||||
self._update_distance_metrics()
|
||||
self._update_curriculum()
|
||||
|
||||
obs = self._get_obs()
|
||||
reward, per_robot_step_rewards = self._compute_reward(action, previous_action)
|
||||
reward = self._compute_reward(action, previous_action)
|
||||
|
||||
self.cumulative_reward += reward
|
||||
for idx, r_step in enumerate(per_robot_step_rewards):
|
||||
self.robot_rewards[idx] += r_step
|
||||
self.robot_reward += reward
|
||||
|
||||
terminated = self._is_done()
|
||||
truncated = self.step_count >= self.curriculum_episode_limit
|
||||
terminated = self.is_failed
|
||||
truncated = self.step_count >= self.max_episode_steps
|
||||
|
||||
self._update_hud()
|
||||
self._update_leader_visuals()
|
||||
if self.step_count % 120 == 0 and self.use_gui:
|
||||
self._update_hud()
|
||||
return obs, reward, terminated, truncated, {}
|
||||
|
||||
def _update_distance_metrics(self):
|
||||
pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
|
||||
self.episode_height_sum += float(pos[2])
|
||||
start_x, start_y, _ = self.start_position
|
||||
dist = float(np.linalg.norm(np.array([pos[0] - start_x, pos[1] - start_y], dtype=np.float32)))
|
||||
self.max_distance_from_start = max(self.max_distance_from_start, dist)
|
||||
self.max_survival_steps = max(self.max_survival_steps, self.step_count)
|
||||
|
||||
def _phase_progress_ready(self, next_phase: CurriculumPhase) -> bool:
|
||||
if next_phase not in self.curriculum_stage_requirements or not self.pb_robots:
|
||||
if next_phase not in self.curriculum_stage_requirements:
|
||||
return False
|
||||
|
||||
req = self.curriculum_stage_requirements[next_phase]
|
||||
|
||||
# 1. Survival Step Check
|
||||
# 1. Survival Check
|
||||
survival_ok = self.max_survival_steps >= req["survival_steps"]
|
||||
|
||||
# 2. Body Posture & Tilt Check
|
||||
position, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robots[0])
|
||||
stability_ok = abs(roll) <= req["stability_roll_pitch"] and abs(pitch) <= req["stability_roll_pitch"]
|
||||
# 2. Smooth Average Stability Checks (Prevents 1-frame spikes from failing curriculum)
|
||||
avg_roll = self.episode_roll_sum / max(1, self.step_count)
|
||||
avg_pitch = self.episode_pitch_sum / max(1, self.step_count)
|
||||
max_allowed_angle = req.get("max_avg_roll_pitch", 0.20)
|
||||
stability_ok = (avg_roll <= max_allowed_angle) and (avg_pitch <= max_allowed_angle)
|
||||
|
||||
# 3. Average Height Ratio Check Across the Episode
|
||||
# 3. Average Height Check
|
||||
avg_height = self.episode_height_sum / max(1, self.step_count)
|
||||
required_min_avg_height = self.target_height * req.get("min_avg_height_ratio", 0.85)
|
||||
height_ok = avg_height >= required_min_avg_height
|
||||
|
||||
# 4. Distance and Displacement Checks
|
||||
start_x, start_y, _ = self.start_positions[0]
|
||||
dx = position[0] - start_x
|
||||
dy = position[1] - start_y
|
||||
# 4. Distance and Drift Checks
|
||||
pos, _ = self.sim_manager.get_robot_pose(self.pb_robot)
|
||||
start_x, start_y, _ = self.start_position
|
||||
dx = pos[0] - start_x
|
||||
dy = pos[1] - start_y
|
||||
dist_2d = math.hypot(dx, dy)
|
||||
|
||||
distance_ok = True
|
||||
@@ -368,203 +340,243 @@ class JackBotEnv(gym.Env):
|
||||
elif "min_distance" in req:
|
||||
distance_ok = dist_2d >= req["min_distance"]
|
||||
|
||||
displacement_ok = True
|
||||
if "max_displacement" in req:
|
||||
displacement_ok = dist_2d <= req["max_displacement"]
|
||||
|
||||
drift_ok = True
|
||||
if "max_lateral_drift" in req:
|
||||
drift_ok = abs(dy) <= req["max_lateral_drift"]
|
||||
|
||||
return survival_ok and height_ok and stability_ok and displacement_ok and distance_ok and drift_ok
|
||||
|
||||
return survival_ok and height_ok and stability_ok and distance_ok and drift_ok
|
||||
|
||||
def _update_curriculum(self):
|
||||
self._curriculum_advanced = False
|
||||
forced_forward = (self.curriculum_phase < CurriculumPhase.FORWARD) and (self.step_count >= 2500)
|
||||
|
||||
if self.curriculum_phase < CurriculumPhase.FORWARD and self._phase_progress_ready(CurriculumPhase.FORWARD):
|
||||
if self.curriculum_phase < CurriculumPhase.FORWARD and (self._phase_progress_ready(CurriculumPhase.FORWARD) or forced_forward):
|
||||
self.curriculum_phase = CurriculumPhase.FORWARD
|
||||
self.curriculum_episode_limit = min(self.max_episode_steps, 600)
|
||||
self._curriculum_advanced = True
|
||||
print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
|
||||
reason = "FORCED (2500 steps)" if forced_forward else "MET"
|
||||
print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked [{reason}] at step {self.step_count}")
|
||||
|
||||
elif self.curriculum_phase < CurriculumPhase.TURN_AND_DIRECTION and self._phase_progress_ready(CurriculumPhase.TURN_AND_DIRECTION):
|
||||
self.curriculum_phase = CurriculumPhase.TURN_AND_DIRECTION
|
||||
self.curriculum_episode_limit = min(self.max_episode_steps, 800)
|
||||
self._curriculum_advanced = True
|
||||
print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
|
||||
|
||||
elif self.curriculum_phase < CurriculumPhase.OMNI_DIRECTION and self._phase_progress_ready(CurriculumPhase.OMNI_DIRECTION):
|
||||
self.curriculum_phase = CurriculumPhase.OMNI_DIRECTION
|
||||
self.curriculum_episode_limit = min(self.max_episode_steps, 1000)
|
||||
self._curriculum_advanced = True
|
||||
print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
|
||||
|
||||
elif self.curriculum_phase < CurriculumPhase.FULL_COMMAND and self._phase_progress_ready(CurriculumPhase.FULL_COMMAND):
|
||||
self.curriculum_phase = CurriculumPhase.FULL_COMMAND
|
||||
self.curriculum_episode_limit = min(self.max_episode_steps, self.max_episode_steps)
|
||||
self._curriculum_advanced = True
|
||||
print(f"[Curriculum] Phase {self.curriculum_phase.name} unlocked at total step {self.total_steps}")
|
||||
|
||||
def _compute_reward(self, action: np.ndarray, previous_action: np.ndarray) -> Tuple[float, list[float]]:
|
||||
rewards = []
|
||||
current_actions = action.reshape(1, 18)
|
||||
previous_actions = previous_action.reshape(1, 18)
|
||||
def _compute_reward(self, action: np.ndarray, previous_action: np.ndarray) -> float:
|
||||
"""
|
||||
Calculates task rewards using normalized Exponential Kernels.
|
||||
Includes a deadband filter for jittering and zero-reward gating when stationary.
|
||||
"""
|
||||
# 1. Fetch Robot State
|
||||
pos, (roll, pitch, yaw) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robot)
|
||||
linear_vel, angular_vel = self.sim_manager.get_robot_velocity(self.pb_robot)
|
||||
current_joints = np.array(
|
||||
self.sim_manager.get_robot_joint_angles(self.pb_robot, self.joint_indices),
|
||||
dtype=np.float32
|
||||
)
|
||||
|
||||
for idx, pb_id in enumerate(self.pb_robots):
|
||||
pos, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(pb_id)
|
||||
linear_vel, angular_vel = self.sim_manager.get_robot_velocity(pb_id)
|
||||
command = self.commands[idx]
|
||||
cmd_vx, cmd_vy, _, cmd_yaw = self.command
|
||||
cmd_norm = math.hypot(cmd_vx, cmd_vy)
|
||||
|
||||
cmd_vx = command[0] # Normalized -1.0 to 1.0
|
||||
cmd_vy = command[1] # Normalized -1.0 to 1.0
|
||||
cmd_yaw = command[3]
|
||||
# 2. Velocity Deadband Filtering (Ignores jittering & micro-movements)
|
||||
VEL_DEADBAND = 0.04 # 4 cm/s threshold
|
||||
YAW_DEADBAND = 0.05 # 0.05 rad/s threshold
|
||||
|
||||
cmd_dir = np.array([cmd_vx, cmd_vy], dtype=np.float32)
|
||||
cmd_norm = np.linalg.norm(cmd_dir)
|
||||
raw_speed = math.hypot(linear_vel[0], linear_vel[1])
|
||||
if raw_speed < VEL_DEADBAND:
|
||||
filtered_vx, filtered_vy = 0.0, 0.0
|
||||
filtered_speed = 0.0
|
||||
else:
|
||||
filtered_vx, filtered_vy = linear_vel[0], linear_vel[1]
|
||||
filtered_speed = raw_speed
|
||||
|
||||
actual_vel_2d = np.array([linear_vel[0], linear_vel[1]], dtype=np.float32)
|
||||
raw_yaw_rate = abs(angular_vel[2])
|
||||
if raw_yaw_rate < YAW_DEADBAND:
|
||||
filtered_yaw_rate = 0.0
|
||||
else:
|
||||
filtered_yaw_rate = angular_vel[2]
|
||||
|
||||
# -----------------------------------------------------------------
|
||||
# 1. NORMALIZED SPEED TRACKING (1.0 = v_max)
|
||||
# -----------------------------------------------------------------
|
||||
if cmd_norm > 0.05:
|
||||
unit_cmd_dir = cmd_dir / cmd_norm
|
||||
aligned_speed = float(np.dot(actual_vel_2d, unit_cmd_dir))
|
||||
# 3. Posture & Stability Sub-Rewards
|
||||
height_error = pos[2] - self.target_height
|
||||
r_height = math.exp(-150.0 * (height_error ** 2))
|
||||
|
||||
target_speed = cmd_norm * self.max_robot_speed # e.g., 0.35 * 0.8 = 0.28 m/s
|
||||
orientation_error = roll**2 + pitch**2
|
||||
r_stability = math.exp(-25.0 * orientation_error)
|
||||
|
||||
if aligned_speed <= 0.0:
|
||||
# Zero or backward movement gets ZERO reward
|
||||
linear_speed_reward = 0.0
|
||||
elif aligned_speed <= target_speed:
|
||||
# Smoothly scales from 0.0 to +5.0 as speed approaches target
|
||||
linear_speed_reward = 5.0 * (aligned_speed / target_speed)
|
||||
else:
|
||||
# Gently penalize overspeeding beyond target
|
||||
overspeed_ratio = (aligned_speed - target_speed) / target_speed
|
||||
linear_speed_reward = max(0.0, 5.0 - 2.5 * overspeed_ratio)
|
||||
joint_error = np.mean(np.square(current_joints - self.default_joint_angles))
|
||||
r_pose = math.exp(-2.0 * joint_error)
|
||||
|
||||
# Drift penalty for sideways sliding
|
||||
perp_vel = actual_vel_2d - aligned_speed * unit_cmd_dir
|
||||
drift_penalty = 0.35 * float(np.dot(perp_vel, perp_vel))
|
||||
else:
|
||||
aligned_speed = 0.0
|
||||
linear_speed_reward = 0.0
|
||||
drift_penalty = 1.0 * (linear_vel[0]**2 + linear_vel[1]**2)
|
||||
action_delta = np.mean(np.square(action - previous_action))
|
||||
r_smoothness = math.exp(-0.1 * action_delta)
|
||||
|
||||
# -----------------------------------------------------------------
|
||||
# 2. TIME-ACCUMULATING STILLNESS PENALTY
|
||||
# -----------------------------------------------------------------
|
||||
actual_yaw_rate = angular_vel[2]
|
||||
is_moving = abs(aligned_speed) > 0.03 or abs(actual_yaw_rate) > 0.08
|
||||
# 4. Mode Logic
|
||||
if cmd_norm < 0.05 and abs(cmd_yaw) < 0.05:
|
||||
# STANDING MODE: Reward clean posture, height, and stability
|
||||
w_height = 0.35
|
||||
w_stability = 0.35
|
||||
w_pose = 0.20
|
||||
w_smoothness = 0.10
|
||||
|
||||
if (cmd_norm > 0.05 or abs(cmd_yaw) > 0.05) and not is_moving:
|
||||
# Increment counter every step the robot stalls under command
|
||||
self.consecutive_still_steps[idx] += 1
|
||||
else:
|
||||
# Reset counter as soon as robot makes a valid move!
|
||||
self.consecutive_still_steps[idx] = 0
|
||||
|
||||
# Penalty grows by 0.05 every step frozen, capped at -3.5 per step
|
||||
still_penalty = min(3.5, 0.05 * self.consecutive_still_steps[idx])
|
||||
|
||||
# -----------------------------------------------------------------
|
||||
# 3. TURNING & POSTURE PENALTIES
|
||||
# -----------------------------------------------------------------
|
||||
if abs(cmd_yaw) > 0.05:
|
||||
turning_reward = 1.5 * (actual_yaw_rate * cmd_yaw)
|
||||
else:
|
||||
turning_reward = -0.6 * (actual_yaw_rate ** 2)
|
||||
|
||||
alive_reward = 0.20
|
||||
|
||||
# Height drop penalty
|
||||
min_valid_h = self.target_height * 0.90
|
||||
if pos[2] < min_valid_h:
|
||||
drop = (min_valid_h - pos[2]) / self.target_height
|
||||
height_penalty = 4.0 * drop + 20.0 * (drop ** 2)
|
||||
else:
|
||||
height_penalty = 0.0
|
||||
|
||||
stability_penalty = 8 * (roll**2 + pitch**2)
|
||||
|
||||
control_delta = np.abs(current_actions[idx] - previous_actions[idx])
|
||||
large_delta_mask = control_delta > 0.12
|
||||
large_delta_penalty = 0.002 * float(np.sum(np.square(control_delta[large_delta_mask]))) if np.any(large_delta_mask) else 0.0
|
||||
|
||||
r_step = (
|
||||
alive_reward
|
||||
+ linear_speed_reward
|
||||
+ turning_reward
|
||||
- drift_penalty
|
||||
- still_penalty
|
||||
- height_penalty
|
||||
- stability_penalty
|
||||
- large_delta_penalty
|
||||
total_reward = (
|
||||
(w_height * r_height)
|
||||
+ (w_stability * r_stability)
|
||||
+ (w_pose * r_pose)
|
||||
+ (w_smoothness * r_smoothness)
|
||||
)
|
||||
rewards.append(r_step)
|
||||
else:
|
||||
# WALKING / TURNING MODE
|
||||
is_moving = (filtered_speed > 0.0) or (abs(filtered_yaw_rate) > 0.0)
|
||||
|
||||
return float(np.sum(rewards)), rewards
|
||||
|
||||
def _is_done(self) -> bool:
|
||||
"""Returns True when the robot has entered a failed state."""
|
||||
return bool(self.failed_robots_mask[0]) if self.failed_robots_mask else False
|
||||
# HARD GATE: If commanded to move but standing still/jittering, reward is strictly 0.0
|
||||
if not is_moving:
|
||||
return 0.0
|
||||
|
||||
target_vx = cmd_vx * self.max_robot_speed
|
||||
target_vy = cmd_vy * self.max_robot_speed
|
||||
|
||||
lin_vel_error = (filtered_vx - target_vx)**2 + (filtered_vy - target_vy)**2
|
||||
r_lin_vel = math.exp(-25.0 * lin_vel_error)
|
||||
|
||||
ang_vel_error = (filtered_yaw_rate - cmd_yaw)**2
|
||||
r_ang_vel = math.exp(-15.0 * ang_vel_error)
|
||||
|
||||
# Stillness Check: Commanded to move, but staying virtually still
|
||||
stillness_penalty = 0.0
|
||||
if math.hypot(target_vx, target_vy) > 0.08 and math.hypot(linear_vel[0], linear_vel[1]) < 0.03:
|
||||
r_lin_vel = 0.0 # Strip velocity credit completely
|
||||
stillness_penalty = -0.25
|
||||
|
||||
w_lin_vel = 0.55
|
||||
w_ang_vel = 0.15
|
||||
w_height = 0.10
|
||||
w_stability = 0.12
|
||||
w_smoothness = 0.08
|
||||
|
||||
total_reward = (
|
||||
(w_lin_vel * r_lin_vel)
|
||||
+ (w_ang_vel * r_ang_vel)
|
||||
+ (w_height * r_height)
|
||||
+ (w_stability * r_stability)
|
||||
+ (w_smoothness * r_smoothness)
|
||||
+ stillness_penalty
|
||||
)
|
||||
|
||||
# Scaled reward for policy stability
|
||||
return float(total_reward / 10.0)
|
||||
|
||||
def _update_hud(self):
|
||||
if not self.use_gui or not self.pb_robots:
|
||||
if not self.use_gui:
|
||||
return
|
||||
|
||||
now = time.time()
|
||||
fps = 1.0 / max(now - self.last_time, 1e-5)
|
||||
self.last_time = now
|
||||
|
||||
heights = []
|
||||
rolls = []
|
||||
pitches = []
|
||||
for pb_id in self.pb_robots:
|
||||
pos, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(pb_id)
|
||||
heights.append(pos[2])
|
||||
rolls.append(math.degrees(roll))
|
||||
pitches.append(math.degrees(pitch))
|
||||
|
||||
avg_height = float(np.mean(heights))
|
||||
avg_roll_pitch = (float(np.mean(rolls)), float(np.mean(pitches)))
|
||||
|
||||
pos, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robot)
|
||||
|
||||
self.hud.update(
|
||||
episode=self.episode_count,
|
||||
step=self.total_steps,
|
||||
robot_rewards=self.robot_rewards,
|
||||
cmd_vel=self.commands[0],
|
||||
robot_rewards=[self.robot_reward],
|
||||
cmd_vel=self.command,
|
||||
fps=fps,
|
||||
avg_height=avg_height,
|
||||
roll_pitch=avg_roll_pitch
|
||||
height=pos[2],
|
||||
roll_pitch=(math.degrees(roll), math.degrees(pitch))
|
||||
)
|
||||
|
||||
def _update_leader_visuals(self):
|
||||
if not self.use_gui:
|
||||
def _update_robot_failure(self):
|
||||
if self.is_failed:
|
||||
return
|
||||
|
||||
best_idx = int(np.argmax(self.robot_rewards))
|
||||
leader_pb_id = self.pb_robots[best_idx]
|
||||
leader_pos, _ = self.sim_manager.get_robot_pose(leader_pb_id)
|
||||
self.leader_crown.update(leader_pos)
|
||||
if self.step_count < 15:
|
||||
return
|
||||
|
||||
def _update_robot_failures(self):
|
||||
"""Checks failure condition and colors failed robots dark gray."""
|
||||
for idx, pb_id in enumerate(self.pb_robots):
|
||||
if self.failed_robots_mask[idx]:
|
||||
continue
|
||||
position, (roll, pitch, _) = self.sim_manager.get_robot_pose_and_rpy(self.pb_robot)
|
||||
collapse_threshold = max(0.04, 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
|
||||
|
||||
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)
|
||||
if is_tilted or is_collapsed:
|
||||
self.is_failed = True
|
||||
if self.use_gui:
|
||||
self.sim_manager.set_robot_color(self.pb_robot, COLOR_FAILED)
|
||||
|
||||
def close(self):
|
||||
self.sim_manager.disconnect()
|
||||
self.sim_manager.disconnect()
|
||||
|
||||
|
||||
class CurriculumCallback(BaseCallback):
|
||||
"""Logs curriculum phase breakdown and best performance metrics to TensorBoard."""
|
||||
|
||||
def __init__(self, verbose=0):
|
||||
super().__init__(verbose)
|
||||
self.best_speed = 0.0
|
||||
self.best_yaw_rate = 0.0
|
||||
self.best_distance = 0.0
|
||||
self.best_survival_steps = 0.0
|
||||
self.best_reward = -float('inf')
|
||||
|
||||
def _on_step(self) -> bool:
|
||||
return True
|
||||
|
||||
def _on_rollout_end(self) -> bool:
|
||||
try:
|
||||
vec_env = self.training_env
|
||||
alive_metrics = vec_env.env_method("get_current_robot_metrics")
|
||||
|
||||
self.best_speed = 0.0
|
||||
self.best_yaw_rate = 0.0
|
||||
self.best_distance = 0.0
|
||||
self.best_survival_steps = 0.0
|
||||
self.best_reward = -float('inf')
|
||||
|
||||
phase_counts = {
|
||||
"stand_only": 0,
|
||||
"forward": 0,
|
||||
"turn_and_direction": 0,
|
||||
"omni_direction": 0,
|
||||
"full_command": 0,
|
||||
}
|
||||
|
||||
for worker_res in alive_metrics:
|
||||
for metrics in worker_res:
|
||||
if not metrics.get("alive", False):
|
||||
continue
|
||||
|
||||
phase_key = metrics.get("phase_name", "STAND_ONLY").lower()
|
||||
if phase_key in phase_counts:
|
||||
phase_counts[phase_key] += 1
|
||||
|
||||
if metrics["reward"] > self.best_reward:
|
||||
self.best_reward = float(metrics["reward"])
|
||||
if metrics["speed"] > self.best_speed:
|
||||
self.best_speed = float(metrics["speed"])
|
||||
if metrics["yaw_rate"] > self.best_yaw_rate:
|
||||
self.best_yaw_rate = float(metrics["yaw_rate"])
|
||||
if metrics["distance_from_start"] > self.best_distance:
|
||||
self.best_distance = float(metrics["distance_from_start"])
|
||||
if metrics["survival_steps"] > self.best_survival_steps:
|
||||
self.best_survival_steps = float(metrics["survival_steps"])
|
||||
|
||||
for phase_name, count in phase_counts.items():
|
||||
self.logger.record(f"phase/{phase_name}", count)
|
||||
|
||||
self.logger.record("custom/best_reward", float(self.best_reward) if np.isfinite(self.best_reward) else 0.0)
|
||||
self.logger.record("custom/best_survival_steps", float(self.best_survival_steps))
|
||||
self.logger.record("custom/best_distance_from_start_m", float(self.best_distance))
|
||||
self.logger.record("custom/best_speed_mps", float(self.best_speed))
|
||||
self.logger.record("custom/best_yaw_rate_rads", float(self.best_yaw_rate))
|
||||
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return True
|
||||
Reference in New Issue
Block a user