Johnny Fernandes
3b4c99a6c4
* training/bc/collect.py: --use-webots-preset now picks the
drive-matched variant. Mecanum drives get HERDING_MEC_WEBOTS
(with the Webots-calibrated strafe efficiency and bleed) so the
collected demos reflect the imperfect physical mecanum the
deployed policy will see. Differential drives still use
HERDING_WEBOTS (no behaviour change there).
* training/rl/train.py: mecanum fine-tune now *unconditionally*
applies the HERDING_MEC_WEBOTS robot config to the PPO env (the
policy must update against the same imperfect kinematics it
deploys on). Diff fine-tune unchanged.
To retrain a mecanum policy end-to-end against the new proto:
python -m training.bc.collect --drive-mode mecanum --world field \
--use-webots-preset \
--out training/bc/demos_mecanum_field_v2.npz
python -m training.bc.pretrain --demos training/bc/demos_mecanum_field_v2.npz \
--out training/runs/bc_mecanum_field_v2 ...
python -m training.rl.train --bc training/runs/bc_mecanum_field_v2 \
--out training/runs/rl_mecanum_field_v2 \
--drive-mode mecanum --world field --use-webots-preset
The same flow for field_round / mecanum/round.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
289 lines
13 KiB
Python
289 lines
13 KiB
Python
"""Collect (obs, action) demonstrations from an analytic teacher.
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Runs the chosen teacher across a grid of ``(n_sheep, seed)`` combos at
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full difficulty, logs every Nth ``(obs, action)`` pair, and saves
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successful trajectories to ``.npz`` for behaviour cloning. The teacher
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is wrapped in :class:`ActiveScanTeacher` by default so it operates on
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the same partial-obs view the student will have at deployment.
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Usage::
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python -m training.bc.collect --teacher strombom \\
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--out training/bc/demos.npz --frame-stack 4
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"""
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from __future__ import annotations
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import argparse
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import os
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import time
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from pathlib import Path
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import numpy as np
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# Configure field geometry before other herding imports read it at module level.
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from herding.world.geometry import configure_from_args as _configure_from_args
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_configure_from_args()
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from herding.control.active_scan import ActiveScanTeacher
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from herding.world.geometry import PEN_ENTRY, FIELD_SHAPE
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from herding.control.sequential import compute_action as sequential_action
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from herding.control.strombom import compute_action as strombom_action
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from herding.control.universal import compute_action as universal_action
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from training.herding_env import HerdingEnv
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TEACHERS = {
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"sequential": sequential_action,
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"strombom": strombom_action,
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"universal": universal_action,
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}
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def _call_teacher(fn, dog_xy, dog_heading, sheep_positions, pen_target,
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drive_mode="differential"):
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"""Call any teacher function and return (vx, vy, omega, mode).
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Normalizes across 3-tuple teachers (vx, vy, mode) and 4-tuple
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universal teacher (vx, vy, omega, mode). ActiveScanTeacher (when
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invoked with drive_mode="mecanum") propagates the base teacher's
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omega — see test_active_scan_preserves_mecanum_omega.
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"""
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# The universal teacher and ActiveScanTeacher accept the extended
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# (dog_xy, heading, sheep, pen, drive_mode) signature. Older
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# teachers accept (dog_xy, sheep, pen). Detect by trying the
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# extended call first.
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try:
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result = fn(dog_xy, dog_heading, sheep_positions, pen_target,
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drive_mode)
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except TypeError:
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try:
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result = fn(dog_xy, dog_heading, sheep_positions, pen_target)
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except TypeError:
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result = fn(dog_xy, sheep_positions, pen_target)
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if len(result) == 4:
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return result # (vx, vy, omega, mode)
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vx, vy, mode = result
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return vx, vy, 0.0, mode
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def collect_one(n_sheep: int, seed: int, max_steps: int, subsample: int,
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teacher_fn, frame_stack: int = 1, privileged: bool = False,
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drive_mode: str = "differential", herding_cfg=None,
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actor_policy=None):
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"""Collect (obs, teacher_action) pairs from one episode.
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``actor_policy`` (DAgger mode): a callable ``policy(obs) -> action`` that
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drives the env. The teacher still labels each visited state. If ``None``
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(default), the teacher drives.
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"""
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env = HerdingEnv(n_sheep=n_sheep, max_steps=max_steps,
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difficulty=1.0, seed=seed, frame_stack=frame_stack,
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drive_mode=drive_mode, herding_cfg=herding_cfg)
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obs, _ = env.reset(seed=seed)
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obs_list, action_list = [], []
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scan_teacher = ActiveScanTeacher(teacher_fn)
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for step in range(max_steps):
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if privileged:
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positions = {f"s{i}": (float(env.sheep_x[i]), float(env.sheep_y[i]))
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for i in range(env.n_sheep) if not env.sheep_penned[i]}
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if not positions:
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break
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vx, vy, omega, _mode = _call_teacher(
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teacher_fn, (env.dog_x, env.dog_y), env.dog_heading,
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positions, PEN_ENTRY, drive_mode,
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)
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else:
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positions = env.perceived_positions()
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result = _call_teacher(
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scan_teacher, (env.dog_x, env.dog_y), env.dog_heading,
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positions, PEN_ENTRY, drive_mode,
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)
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vx, vy, omega, _mode = result
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if drive_mode == "mecanum":
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teacher_action = np.array([vx, vy, omega], dtype=np.float32)
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else:
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teacher_action = np.array([vx, vy], dtype=np.float32)
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if step % subsample == 0:
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obs_list.append(obs.copy())
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action_list.append(teacher_action.copy())
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# In DAgger mode the policy drives; otherwise the teacher does.
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step_action = (actor_policy(obs) if actor_policy is not None
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else teacher_action)
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obs, _r, term, trunc, _info = env.step(step_action)
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if term or trunc:
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break
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success = bool(env.sheep_penned.all())
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return (
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np.asarray(obs_list, dtype=np.float32),
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np.asarray(action_list, dtype=np.float32),
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success,
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env.steps,
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)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="training/bc/demos.npz")
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parser.add_argument("--n-sheep-list", default="1,2,3,5,8,10")
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parser.add_argument("--seeds-per-n", type=int, default=15)
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parser.add_argument("--max-steps", type=int, default=30000)
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parser.add_argument("--subsample", type=int, default=5,
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help="Keep every Nth (obs, action) pair.")
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parser.add_argument("--keep-failures", action="store_true",
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help="Include partial-success trajectories. Default off.")
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parser.add_argument("--teacher", default="universal",
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choices=list(TEACHERS.keys()),
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help="Which analytic teacher to demonstrate.")
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parser.add_argument("--frame-stack", type=int, default=1,
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help="Concatenate the last K obs into a "
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"(32·K)-D vector for the policy.")
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parser.add_argument("--privileged", action="store_true",
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help="Teacher reads ground truth instead of "
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"tracker output (asymmetric BC).")
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parser.add_argument("--drive-mode", default="differential",
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choices=["differential", "mecanum"],
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help="Drive mode for the dog robot.")
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parser.add_argument("--world", default=None,
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choices=["field", "field_round"],
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help="World shape. If not set, uses HERDING_WORLD "
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"env var or defaults to 'field'. Must be set "
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"before geometry is imported.")
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# Domain randomisation — applied to the gym env during collection so
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# the teacher demonstrates under the same noise the policy will face.
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parser.add_argument("--fp-rate", type=float, default=0.0,
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help="Mean false-positive detections injected per "
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"step (Poisson λ). 0 = clean sim (default).")
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parser.add_argument("--action-smooth", type=float, default=0.0,
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help="EMA coefficient on dog actions (0 = none). "
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"Set to 0.55 to match the Webots controller.")
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parser.add_argument("--wheel-slip-std", type=float, default=0.0,
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help="Gaussian noise (rad/s) on wheel speeds for "
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"mecanum dynamics domain randomisation.")
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parser.add_argument("--dagger-policy", default=None,
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help="Path to a BC/PPO policy directory. When set, "
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"the policy drives the env (DAgger) while the "
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"teacher labels every visited state.")
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parser.add_argument("--use-webots-preset", action="store_true",
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help="Use HERDING_WEBOTS preset (140° FOV + tight "
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"tracker). Match this to deployment for DAgger.")
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args = parser.parse_args()
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# Validate --world matches geometry (already configured by the
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# early pre-parse above, or by HERDING_WORLD env var).
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if args.world is not None and args.world != FIELD_SHAPE:
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print(f"[demos] WARNING: --world={args.world} but geometry is "
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f"'{FIELD_SHAPE}'. This should not happen — file a bug.")
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from herding.config import (
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HerdingConfig, HERDING_WEBOTS, HERDING_MEC_WEBOTS,
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DomainRandomConfig, RobotConfig,
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)
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if args.use_webots_preset:
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# Pick the drive-matched Webots preset — for mecanum we use the
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# variant that simulates the physical-roller proto's strafe
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# efficiency and forward bleed so the policy trains under the
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# same imperfect kinematics it sees at deployment.
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base = HERDING_MEC_WEBOTS if args.drive_mode == "mecanum" else HERDING_WEBOTS
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herding_cfg = base.replace(
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domain_random=DomainRandomConfig(
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fp_rate=args.fp_rate,
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wheel_slip_std=args.wheel_slip_std,
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),
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robot=RobotConfig(
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action_smooth=args.action_smooth,
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strafe_efficiency=base.robot.strafe_efficiency,
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strafe_to_forward_bleed=base.robot.strafe_to_forward_bleed,
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),
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)
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preset_name = "HERDING_MEC_WEBOTS" if args.drive_mode == "mecanum" else "HERDING_WEBOTS"
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print(f"[demos] {preset_name} preset + DR: fp_rate={args.fp_rate} "
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f"action_smooth={args.action_smooth} wheel_slip_std={args.wheel_slip_std} "
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f"strafe_eff={herding_cfg.robot.strafe_efficiency:.2f}")
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else:
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herding_cfg = None
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if args.fp_rate > 0.0 or args.action_smooth > 0.0 or args.wheel_slip_std > 0.0:
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herding_cfg = HerdingConfig(
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domain_random=DomainRandomConfig(
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fp_rate=args.fp_rate,
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wheel_slip_std=args.wheel_slip_std,
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),
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robot=RobotConfig(action_smooth=args.action_smooth),
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)
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print(f"[demos] domain-random: fp_rate={args.fp_rate} "
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f"action_smooth={args.action_smooth} "
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f"wheel_slip_std={args.wheel_slip_std}")
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actor_policy = None
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if args.dagger_policy is not None:
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# DAgger: failures are the most valuable data (off-policy states
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# where the student needs teacher correction). Always keep them.
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args.keep_failures = True
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from stable_baselines3 import PPO
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from pathlib import Path as _P
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run = _P(args.dagger_policy)
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for name in ("policy.zip", "final.zip"):
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if (run / name).exists():
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zip_path = run / name
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break
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else:
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raise FileNotFoundError(
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f"No policy found in {run} (tried policy.zip, final.zip)")
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_model = PPO.load(str(zip_path), device="auto")
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print(f"[demos] DAgger mode: actor = {zip_path}")
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def actor_policy(obs):
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obs_b = np.asarray(obs, dtype=np.float32).reshape(1, -1)
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a, _ = _model.predict(obs_b, deterministic=True)
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return a[0]
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teacher_fn = TEACHERS[args.teacher]
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print(f"[demos] teacher: {args.teacher} world: {FIELD_SHAPE}")
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n_sheep_list = [int(x) for x in args.n_sheep_list.split(",")]
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print(f"[demos] grid: n_sheep={n_sheep_list}, seeds={args.seeds_per_n}, "
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f"max_steps={args.max_steps}, subsample={args.subsample}")
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all_obs, all_actions, all_meta = [], [], []
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t_start = time.time()
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n_success = 0; n_total = 0
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for n in n_sheep_list:
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for seed in range(args.seeds_per_n):
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obs, actions, success, total_steps = collect_one(
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n, seed, args.max_steps, args.subsample, teacher_fn,
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frame_stack=args.frame_stack, privileged=args.privileged,
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drive_mode=args.drive_mode, herding_cfg=herding_cfg,
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actor_policy=actor_policy,
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)
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n_total += 1
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if success:
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n_success += 1
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keep = success or args.keep_failures
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if keep and len(obs) > 0:
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all_obs.append(obs)
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all_actions.append(actions)
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all_meta.append((n, seed, len(obs), int(success), total_steps))
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tag = "✓" if success else "✗"
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print(f" [{tag}] n={n:>2d} seed={seed:>2d} steps={total_steps:>6d} "
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f"logged={len(obs):>5d}")
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if not all_obs:
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raise RuntimeError("No trajectories kept — try --keep-failures.")
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obs = np.concatenate(all_obs, axis=0)
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actions = np.concatenate(all_actions, axis=0)
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meta = np.array(all_meta, dtype=np.int32)
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Path(args.out).parent.mkdir(parents=True, exist_ok=True)
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np.savez(args.out, obs=obs, actions=actions, meta=meta)
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elapsed = time.time() - t_start
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print(f"\n=== {n_success}/{n_total} trajectories successful ({100*n_success/n_total:.0f}%) ===")
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print(f"=== {len(obs)} transitions saved to {args.out} ===")
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print(f"=== obs={obs.shape}, actions={actions.shape}, elapsed={elapsed:.0f}s ===")
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if __name__ == "__main__":
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main()
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