Johnny Fernandes
508 lines
21 KiB
Python
508 lines
21 KiB
Python
"""Shepherd Dog controller (Webots).
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Mode is selected by ``HERDING_MODE`` (env var, or via the
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``herding_runtime.cfg`` file the launcher writes since Webots strips
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env vars on some setups):
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strombom → canonical Strömbom collect/drive heuristic.
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sequential → single-target "pin and push" — drives the sheep
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closest to the pen.
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bc → behaviour-cloned MLP, trained on Strömbom demos via
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sim. Default policy directory: training/runs/bc_v3.
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rl → KL-regularised PPO fine-tune of the BC policy. Same
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obs/action space as bc; refines time-to-pen via
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environment reward while staying anchored to bc.
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Default policy directory: training/runs/rl_v1.
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dagger → DAgger data collection. Reads sheep ground-truth
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via the receiver, computes the active-scan teacher's
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recommended action at every step, drives with either
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the teacher (HERDING_DAGGER_DRIVER=teacher, default)
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or the loaded student (=student), and logs each
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(lidar_stacked_obs, teacher_action) pair. On exit
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dumps to ``training/dagger/dagger_<ts>.npz`` for
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``tools.dagger_merge_train`` to consume.
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Sheep perception
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----------------
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The dog now perceives sheep through its **front-mounted 140° LiDAR**
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(``protos/ShepherdDog.proto``: 180 rays, 12 m max range). Each step
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the controller:
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1. Reads ``lidar.getRangeImage()``.
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2. Runs ``herding.lidar_perception.detections_from_scan`` to cluster
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returns into world-frame ``(x, y)`` sheep estimates.
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3. Folds those into a ``herding.sheep_tracker.SheepTracker`` which
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maintains last-seen positions for sheep currently out of the
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FOV and latches "penned" once a track disappears near the gate.
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The output of step 3 is a ``{name: (x, y)}`` dict shaped exactly like
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the receiver-based one we used to consume — so Strömbom, Sequential
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and the BC obs builder run unchanged. The sheep→dog Emitter/Receiver
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link is still up (kept passively for compatibility) but its messages
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are *not* used for control.
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All modes share the same low-level differential-drive controller
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(``herding.diffdrive.velocity_to_wheels`` with cos(err)-clamped forward
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speed), so switching modes does not retune actuation.
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A safety supervisor enforces the "dog stays out of the pen" invariant:
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if the action would push the dog past ``DOG_SOUTH_LIMIT`` it is
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overridden with a north-driving correction. RL fallback: if the policy
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zip can't be loaded (SB3 missing, file missing), the controller drops
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to strombom mode automatically.
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"""
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import math
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import os
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import sys
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# --- Make the shared herding/ package importable from this controller dir ---
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_HERE = os.path.dirname(os.path.abspath(__file__))
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_PROJECT_ROOT = os.path.normpath(os.path.join(_HERE, "..", ".."))
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if _PROJECT_ROOT not in sys.path:
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sys.path.insert(0, _PROJECT_ROOT)
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from controller import Robot
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from herding.active_scan import ActiveScanTeacher
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from herding.control import modulate_speed_near_sheep
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from herding.diffdrive import velocity_to_wheels
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from herding.geometry import (
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DOG_MAX_LINEAR, DOG_MAX_WHEEL_OMEGA,
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DOG_SOUTH_LIMIT, DOG_WHEEL_RADIUS,
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PEN_ENTRY, is_penned_position,
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)
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from herding.lidar_perception import detections_from_scan
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from herding.obs import OBS_DIM, build_obs
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from herding.sequential import compute_action_debug as sequential_action_debug
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from herding.sheep_tracker import SheepTracker
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from herding.strombom import compute_action as strombom_action
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from herding.strombom import compute_action_debug as strombom_action_debug
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# ---------------------------------------------------------------------------
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# Mode selection
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# ---------------------------------------------------------------------------
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def _load_runtime_config():
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"""Read mode + policy_dir overrides from a runtime config file.
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Webots strips HERDING_* env vars in some configurations, so the
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launcher writes a tiny ``herding_runtime.cfg`` (key=value lines)
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in the project root and the controller reads it here. Env vars
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win if both are present; the file is the fallback.
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"""
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cfg_path = os.path.join(_PROJECT_ROOT, "herding_runtime.cfg")
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if not os.path.exists(cfg_path):
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return {}
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out = {}
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try:
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with open(cfg_path) as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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k, _, v = line.partition("=")
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out[k.strip().upper()] = v.strip()
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except OSError:
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return {}
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return out
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_runtime_cfg = _load_runtime_config()
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MODE = (os.environ.get("HERDING_MODE")
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or _runtime_cfg.get("HERDING_MODE")
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or "bc").lower()
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def _resolve_policy_dir(mode: str) -> str:
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"""Where to look for the trained policy for the given mode.
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Priority:
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1. HERDING_POLICY_DIR env var or runtime-cfg entry, if it points
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to a real directory.
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2. Mode-specific default:
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bc → training/runs/bc_v3 (Strömbom-imitated MLP)
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rl → training/runs/rl_v1 (KL-PPO fine-tune of bc_v3)
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3. Fall back to bc_v3.
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All checkpoints are frame-stacked K = 4; ``policy_loader`` reads
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the stacking factor from the policy's observation space.
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"""
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env_dir = (os.environ.get("HERDING_POLICY_DIR")
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or _runtime_cfg.get("HERDING_POLICY_DIR"))
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if env_dir and os.path.isdir(env_dir):
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return env_dir
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mode_default = {
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"bc": os.path.join(_PROJECT_ROOT, "training", "runs", "bc_v3"),
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"rl": os.path.join(_PROJECT_ROOT, "training", "runs", "rl_v1"),
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"dagger": os.path.join(_PROJECT_ROOT, "training", "runs", "bc_v3"),
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}
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primary = mode_default.get(mode, mode_default["bc"])
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if os.path.isdir(primary):
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return primary
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# Fall back to BC if the requested checkpoint isn't there yet
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# (e.g., user asked for `rl` before training the fine-tune).
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fallback = mode_default["bc"]
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if os.path.isdir(fallback):
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return fallback
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return env_dir or primary
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_VALID_MODES = ("bc", "rl", "strombom", "sequential", "dagger", "diag")
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# Back-compat: an old config saying HERDING_MODE=rl meant "the BC policy".
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# We now use `rl` strictly for the KL-PPO fine-tune. If the rl_v1
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# directory isn't present, _resolve_policy_dir below silently falls
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# back to bc_v3, preserving the old behaviour.
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if MODE not in _VALID_MODES:
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print(f"[dog] unknown HERDING_MODE={MODE!r}; defaulting to strombom.")
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MODE = "strombom"
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DAGGER_DRIVER = (os.environ.get("HERDING_DAGGER_DRIVER")
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or _runtime_cfg.get("HERDING_DAGGER_DRIVER")
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or "teacher").lower()
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if DAGGER_DRIVER not in ("teacher", "student"):
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DAGGER_DRIVER = "teacher"
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POLICY_DIR = _resolve_policy_dir(MODE)
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policy_handle = None
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if MODE in ("bc", "rl", "dagger"):
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print(f"[dog] resolved POLICY_DIR={POLICY_DIR} exists={os.path.isdir(POLICY_DIR)}")
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try:
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from policy_loader import load as _load_policy
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policy_handle = _load_policy(POLICY_DIR)
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print(f"[dog] policy loaded from {POLICY_DIR}")
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except Exception as exc:
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if MODE in ("bc", "rl"):
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print(f"[dog] policy load failed ({exc!r}); falling back to strombom.")
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MODE = "strombom"
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else:
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# In dagger mode, no policy is fine if driver=teacher.
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print(f"[dog] policy load failed ({exc!r}); dagger driver forced to teacher.")
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policy_handle = None
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print(f"[dog] running in mode={MODE}"
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+ (f" driver={DAGGER_DRIVER}" if MODE == "dagger" else ""))
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# ---------------------------------------------------------------------------
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# Action smoothing + safety supervisor
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# ---------------------------------------------------------------------------
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ACTION_SMOOTH = 0.55 # was 0.35; bumped for less frame-to-frame action jitter
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prev_action = (0.0, 0.0)
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def safety_clamp(vx: float, vy: float, dog_x: float, dog_y: float) -> tuple:
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"""If the dog is near the south barrier and the action would push it
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further south, override with a northward action. Hard invariant: the
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dog never enters the pen."""
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if dog_y < DOG_SOUTH_LIMIT and vy < 0.0:
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return (0.0, 1.0)
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if dog_y < DOG_SOUTH_LIMIT + 0.5 and vy < -0.2:
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return (vx * 0.5, max(0.0, vy + 0.5))
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return (vx, vy)
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# ---------------------------------------------------------------------------
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# Driving
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# ---------------------------------------------------------------------------
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def drive(vx: float, vy: float, left_motor, right_motor, compass, motor_max: float):
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if math.hypot(vx, vy) < 1e-3:
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left_motor.setVelocity(0.0)
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right_motor.setVelocity(0.0)
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return
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n = compass.getValues()
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h = math.atan2(n[0], n[1])
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left, right = velocity_to_wheels(
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vx, vy, h,
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max_linear=DOG_MAX_LINEAR,
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wheel_radius=DOG_WHEEL_RADIUS,
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max_wheel_omega=motor_max,
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k_turn=4.0,
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)
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left_motor.setVelocity(left)
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right_motor.setVelocity(right)
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# ---------------------------------------------------------------------------
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# Webots devices
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# ---------------------------------------------------------------------------
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robot = Robot()
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timestep = int(robot.getBasicTimeStep())
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left_motor = robot.getDevice("left wheel motor")
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right_motor = robot.getDevice("right wheel motor")
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left_motor.setPosition(float("inf"))
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right_motor.setPosition(float("inf"))
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left_motor.setVelocity(0.0)
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right_motor.setVelocity(0.0)
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MOTOR_MAX = min(left_motor.getMaxVelocity(), DOG_MAX_WHEEL_OMEGA)
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gps = robot.getDevice("gps"); gps.enable(timestep)
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compass = robot.getDevice("compass"); compass.enable(timestep)
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receiver = robot.getDevice("receiver"); receiver.enable(timestep)
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emitter = robot.getDevice("emitter")
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lidar = robot.getDevice("lidar"); lidar.enable(timestep)
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# The receiver channel from sheep is no longer consumed for perception
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# (kept enabled in case any peripheral tooling reads it). Sheep
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# positions come exclusively from the LiDAR + tracker pipeline below.
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tracker = SheepTracker()
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# Cosmetic ear motors — ignored by control logic but keep them animated.
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left_ear = robot.getDevice("left ear motor")
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right_ear = robot.getDevice("right ear motor")
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left_ear.setPosition(float("inf"))
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right_ear.setPosition(float("inf"))
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left_ear.setVelocity(0.0)
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right_ear.setVelocity(0.0)
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ear_phase = 0.0
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EAR_AMPLITUDE = 0.35
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EAR_RATE = 8.0
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# ---------------------------------------------------------------------------
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# Main loop
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# ---------------------------------------------------------------------------
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# Active sheep positions come from the LiDAR-fed tracker each step;
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# penned_set is the tracker's ``get_penned_set()`` call. We drain the
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# receiver queue without consuming it, so the small backlog of sheep
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# pings can't grow unbounded.
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step_count = 0
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import atexit
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import time
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import numpy as _np
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# DAgger state ----------------------------------------------------------
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# Logged each step in dagger mode: (stacked_lidar_obs, teacher_action).
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DAGGER_LOG_OBS: list = []
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DAGGER_LOG_ACT: list = []
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# Diagnostic mode buffer (one dict per step).
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DIAG_BUF: list = []
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# Frame stack buffer the controller maintains itself when dagger mode is
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# active — the stacked obs we log must match what the policy sees so the
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# downstream BC consumes (stacked_obs, teacher_action) pairs cleanly.
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_FRAME_STACK = (policy_handle.frame_stack if policy_handle is not None else 4)
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_dagger_buffer: list = []
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# Active-scan teacher operates on GT (read from receiver).
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_dagger_teacher = ActiveScanTeacher(strombom_action) if MODE == "dagger" else None
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# GT positions accumulated from the receiver (sheep emit their xy each step).
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_gt_sheep: dict = {}
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_DAGGER_RUN_TS = int(time.time()) # one file per controller run
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_DAGGER_DUMPED = False
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# Sentinel that the auto-collection script polls — empty file written
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# when this controller decides the run is "done" (all sheep penned, by
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# GT). The launcher then kills Webots and moves on without waiting out
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# its timeout. Honoured only in dagger mode.
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_DAGGER_DONE_FILE = os.path.join(_PROJECT_ROOT, "training", "dagger", ".DONE")
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def _dump_dagger_log():
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"""Save accumulated (obs, teacher_action) pairs to disk on exit.
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Webots may SIGKILL the controller, so the loop also calls this every
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DAGGER_FLUSH_STEPS so we lose at most a few seconds of data per run.
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Idempotent — repeated calls overwrite the same file with the latest
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accumulated buffer.
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"""
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global _DAGGER_DUMPED
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if MODE != "dagger" or not DAGGER_LOG_OBS:
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return
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out_dir = os.path.join(_PROJECT_ROOT, "training", "dagger")
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os.makedirs(out_dir, exist_ok=True)
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out_path = os.path.join(out_dir, f"dagger_{_DAGGER_RUN_TS}.npz")
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obs_arr = _np.stack(DAGGER_LOG_OBS).astype(_np.float32)
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act_arr = _np.stack(DAGGER_LOG_ACT).astype(_np.float32)
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_np.savez(out_path, obs=obs_arr, actions=act_arr)
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if not _DAGGER_DUMPED:
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print(f"[dog dagger] wrote {len(DAGGER_LOG_OBS)} pairs → {out_path}")
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_DAGGER_DUMPED = True
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DAGGER_FLUSH_STEPS = 500
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atexit.register(_dump_dagger_log)
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while robot.step(timestep) != -1:
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step_count += 1
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# Drain receiver. In every mode we capture GT for the diagnostic
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# log line — perception still comes from LiDAR, the GT is read-only.
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while receiver.getQueueLength() > 0:
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msg = receiver.getString()
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receiver.nextPacket()
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parts = msg.split(":")
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if len(parts) == 4 and parts[0] == "sheep":
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try:
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_gt_sheep[parts[1]] = (float(parts[2]), float(parts[3]))
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except ValueError:
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pass
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pos = gps.getValues()
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dog_xy = (pos[0], pos[1])
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n = compass.getValues()
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dog_heading = math.atan2(n[0], n[1])
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# ---- LiDAR perception → tracker → sheep_positions dict ----
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ranges = _np.asarray(lidar.getRangeImage(), dtype=_np.float32)
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detections = detections_from_scan(ranges, dog_xy[0], dog_xy[1], dog_heading)
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sheep_positions = tracker.update(detections)
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penned_set = tracker.get_penned_set()
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# ---- Diagnostic mode: dump the first DIAG_STEPS scans + GT to disk.
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if MODE == "diag":
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DIAG_STEPS = 80
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if step_count <= DIAG_STEPS:
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DIAG_BUF.append(dict(
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step=step_count,
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ranges=ranges.copy(),
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dog_x=dog_xy[0], dog_y=dog_xy[1], dog_h=dog_heading,
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gt_sheep=dict(_gt_sheep),
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detections=list(detections),
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))
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if step_count == DIAG_STEPS:
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_diag_path = os.path.join(_PROJECT_ROOT, "training", "dagger",
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f"diag_{int(time.time())}.npz")
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os.makedirs(os.path.dirname(_diag_path), exist_ok=True)
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_np.savez(
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_diag_path,
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ranges=_np.stack([d["ranges"] for d in DIAG_BUF]),
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dog_xy=_np.array([[d["dog_x"], d["dog_y"]] for d in DIAG_BUF],
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dtype=_np.float32),
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dog_h=_np.array([d["dog_h"] for d in DIAG_BUF], dtype=_np.float32),
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# Per-step GT serialised: max-pad to 10 sheep.
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gt_xy=_np.array([
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[list(d["gt_sheep"].get(f"sheep{i}", (1e9, 1e9)))
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for i in range(1, 11)]
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for d in DIAG_BUF
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], dtype=_np.float32),
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detections=_np.array([
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len(d["detections"]) for d in DIAG_BUF
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], dtype=_np.int32),
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)
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print(f"[dog diag] wrote {DIAG_STEPS} scans → {_diag_path}")
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# Build the single-frame LiDAR obs (matches what the env produces).
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sheep_xy_list = list(sheep_positions.values())
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sheep_penned_list = [False] * len(sheep_xy_list)
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single_obs = build_obs(dog_xy, dog_heading, sheep_xy_list, sheep_penned_list)
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# Maintain our own frame stack so logged obs == what policy sees.
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if not _dagger_buffer:
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_dagger_buffer = [single_obs.copy() for _ in range(_FRAME_STACK)]
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else:
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_dagger_buffer.append(single_obs)
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if len(_dagger_buffer) > _FRAME_STACK:
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_dagger_buffer = _dagger_buffer[-_FRAME_STACK:]
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stacked_obs = _np.concatenate(_dagger_buffer, axis=0).astype(_np.float32)
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# ---- Action selection ----
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if MODE == "diag":
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# Diagnostic mode: rotate in place so the captured scans cover
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# all 360° of view from one position. Target = heading + π →
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# cos(err) clamps forward to ~0, the dog spins.
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_t = dog_heading + math.pi
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vx, vy = math.cos(_t), math.sin(_t)
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elif MODE == "dagger":
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# Teacher: active-scan + Strömbom on GT (active sheep only).
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gt_active = {name: xy for name, xy in _gt_sheep.items()
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if not is_penned_position(xy[0], xy[1])}
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t_vx, t_vy, _mode_str = _dagger_teacher(
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dog_xy, dog_heading, gt_active, PEN_ENTRY,
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)
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# Student (if a policy is loaded).
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s_vx, s_vy = None, None
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if policy_handle is not None:
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action = policy_handle.predict(stacked_obs)
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s_vx, s_vy = float(action[0]), float(action[1])
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# Drive selection.
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if DAGGER_DRIVER == "student" and policy_handle is not None:
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vx, vy = s_vx, s_vy
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else:
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vx, vy = t_vx, t_vy
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# Always log the teacher action (this is the supervision signal).
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DAGGER_LOG_OBS.append(stacked_obs.copy())
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DAGGER_LOG_ACT.append(_np.array([t_vx, t_vy], dtype=_np.float32))
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elif MODE in ("bc", "rl") and policy_handle is not None:
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# Pass the single-frame obs; the policy_loader maintains its own
|
|
# frame stack internally. Both bc and rl use the same control
|
|
# interface — the only difference is which checkpoint loaded.
|
|
action = policy_handle.predict(single_obs)
|
|
vx, vy = float(action[0]), float(action[1])
|
|
elif MODE in ("strombom", "sequential"):
|
|
# Wrap the analytic teacher in ActiveScanTeacher so the dog
|
|
# rotates / walks-to-centre when the tracker briefly empties,
|
|
# instead of going idle. Without this wrapper, the first 2 s
|
|
# of LiDAR-blind operation kills the run because Strömbom and
|
|
# Sequential both return (0, 0) when there are no positions.
|
|
if "_analytic_teacher" not in globals():
|
|
from herding.sequential import compute_action as sequential_action
|
|
_analytic_teacher = ActiveScanTeacher(
|
|
strombom_action if MODE == "strombom" else sequential_action
|
|
)
|
|
vx, vy, _mode_str = _analytic_teacher(
|
|
dog_xy, dog_heading, sheep_positions, PEN_ENTRY,
|
|
)
|
|
|
|
# Shared post-process: speed modulation near sheep. Applies to bc,
|
|
# rl, strombom, sequential — every mode where the action source is
|
|
# nominally unit-magnitude. In dagger mode the active-scan teacher
|
|
# has already modulated, and the diag mode action is hand-built for
|
|
# rotation; both skip.
|
|
if MODE not in ("dagger", "diag"):
|
|
vx, vy = modulate_speed_near_sheep(vx, vy, dog_xy, sheep_positions)
|
|
|
|
# EMA smoothing — reduces oscillation from policy or Strömbom flips.
|
|
vx = ACTION_SMOOTH * prev_action[0] + (1.0 - ACTION_SMOOTH) * vx
|
|
vy = ACTION_SMOOTH * prev_action[1] + (1.0 - ACTION_SMOOTH) * vy
|
|
|
|
# Safety: dog must never enter the pen.
|
|
vx, vy = safety_clamp(vx, vy, dog_xy[0], dog_xy[1])
|
|
prev_action = (vx, vy)
|
|
|
|
drive(vx, vy, left_motor, right_motor, compass, MOTOR_MAX)
|
|
emitter.send(f"dog:{dog_xy[0]:.4f}:{dog_xy[1]:.4f}")
|
|
|
|
# Cosmetic ear wiggle — purely visual.
|
|
ear_phase += 0.12
|
|
ear_pos = EAR_AMPLITUDE * math.sin(ear_phase)
|
|
left_ear.setVelocity(EAR_RATE)
|
|
right_ear.setVelocity(EAR_RATE)
|
|
left_ear.setPosition(ear_pos)
|
|
right_ear.setPosition(-ear_pos)
|
|
|
|
# --- DAgger: early-stop when all GT sheep are penned ---
|
|
if MODE == "dagger" and _gt_sheep:
|
|
gt_active_count = sum(1 for x, y in _gt_sheep.values()
|
|
if not is_penned_position(x, y))
|
|
if gt_active_count == 0 and not os.path.exists(_DAGGER_DONE_FILE):
|
|
_dump_dagger_log()
|
|
open(_DAGGER_DONE_FILE, "w").close()
|
|
print(f"[dog dagger] all {len(_gt_sheep)} sheep penned — "
|
|
f"wrote {_DAGGER_DONE_FILE}, exiting early")
|
|
|
|
if MODE == "dagger" and step_count % DAGGER_FLUSH_STEPS == 0 and DAGGER_LOG_OBS:
|
|
_dump_dagger_log()
|
|
|
|
if step_count % 200 == 0:
|
|
gt_penned = sum(1 for x, y in _gt_sheep.values()
|
|
if is_penned_position(x, y))
|
|
gt_total = len(_gt_sheep)
|
|
extra = ""
|
|
if MODE == "dagger":
|
|
extra = f" logged={len(DAGGER_LOG_OBS)}"
|
|
print(f"[dog mode={MODE}] step={step_count} "
|
|
f"GT_penned={gt_penned}/{gt_total} "
|
|
f"tracks_active={tracker.n_active()} "
|
|
f"tracks_penned={tracker.n_penned()} "
|
|
f"detections={len(detections)} action=({vx:+.2f}, {vy:+.2f}){extra}")
|
|
|
|
# Loop ended (Webots told us to quit). Flush any remaining DAgger log.
|
|
_dump_dagger_log()
|