Johnny Fernandes
459 lines
21 KiB
Markdown
459 lines
21 KiB
Markdown
# RL-Driven Shepherd Herding — Implementation Plan
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This plan turns the existing Strömbom-only Webots project into a dual-mode
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shepherd controller (RL primary, Strömbom fallback), with a fast Gymnasium
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training environment that mirrors the Webots dynamics tightly enough for
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sim-to-sim transfer. Stable-Baselines3 PPO is the learner.
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---
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## 1. Current state (audit)
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### World geometry — `worlds/field.wbt`
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- Field bounded by stone walls at **x,y ∈ [−15, +15]**. Inside-usable area is
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~[−14.5, 14.5] (`X_MIN/MAX` in `flocking.py`).
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- **Pen is *inside* the field**: x ∈ [10, 13], y ∈ [−15, −8], with the
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opening on its **north** side at y = −8 (post-and-rail fence W/E; open N).
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- South stone wall has a **gate at x ∈ [10, 13], y = −15** (split wall +
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gate posts at x=10 and x=13). So sheep that get penned end up between the
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fence (N side at y=−8) and the south stone wall (with the wooden gate at
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y=−15 currently slightly ajar). The pen is effectively an L-shape inside
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the field, not external.
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- Spawns: dog at origin (0, 0), 3 sheep around (3, ±2) and (4, 0). Two more
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sheep are commented out.
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### Robots — protos
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- **Sheep** (`protos/Sheep.proto`): differential drive, wheel radius 0.031 m,
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axle half-width 0.10 m → wheel base 0.20 m. `maxVelocity = 25 rad/s` →
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max linear ≈ **0.78 m/s**. Sensors: GPS, Compass, Emitter+Receiver on
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channel 1. `supervisor = TRUE` (used to repaint wool pink on pen entry).
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- **ShepherdDog** (`protos/ShepherdDog.proto`): differential drive, wheel
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radius 0.038 m, axle half-width 0.14 m → wheel base 0.28 m.
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`maxVelocity = 70 rad/s` → max linear ≈ **2.66 m/s**. Sensors: GPS,
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Compass, Gyro, Accelerometer, **Lidar** (front-only, FOV 2.44 rad ≈ 140°,
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180 rays, range 0.10–12 m, noise 0.005), Emitter+Receiver on channel 1,
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cosmetic ear/tail motors.
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### Sheep controller — `controllers/sheep/{sheep.py,flocking.py}`
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- Reynolds-style boid stack: flee (quadratic ramp inside FLEE_DIST=7 m),
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cohesion (within 8 m), separation (within 2.5 m), wall soft repulsion
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(margin 5 m), wall hard escape (margin 1 m, gain 50), wander.
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- Pen-aware: sheep below the gate line but outside the gate corridor get a
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northward "deadzone" assist; on first entry into the pen rectangle,
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sheep latches `penned=True`, repaints pink, and switches to in-pen
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containment + jitter.
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- Driver: heading-error PD on diff-drive (k=4), forward velocity scaled by
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`cos(err)`, MAX_SPEED=22 (motor units, capped by proto's 25 rad/s).
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- Stuck detector: if displacement < 0.05 m for 20 steps, drives toward
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field origin to escape wall-pin (a known differential-drive failure mode).
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### Dog controller — `controllers/shepherd_dog/{shepherd_dog.py,strombom.py}`
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- Strömbom collect/drive heuristic. CoM-radius gating
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`radius > F·√n` with F=2 selects collect (push furthest sheep inward) vs
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drive (push CoM toward the pen entry point at (11.5, −8.0)).
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- Deadzone rescue: when a sheep is below the gate line and outside the
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pen's x-corridor, the dog repositions to a "behind the sheep, opposite
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the pen" stand-off so the sheep's flee vector points back through the
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gate. Variants 0/1 alternate lateral offset to break corner cycles.
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- Stuck-rescue, EMA action smoothing, target-deadband, RESCUE_SPEED_CAP,
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cooldown — all empirical fixes for diff-drive oscillation.
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- Logs full per-step debug to `dog_behavior_log.csv` (currently 7 MB —
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add to `.gitignore`).
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### Deleted training scaffolding (per `git status`)
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- `controllers/shepherd_dog_rl/{shepherd_dog_rl.py, final_model.zip, vecnorm.pkl, plot_debug.py}`
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- `training/{config.json, herding_env.py, parity_test.py, requirements.txt, train.py, train_at.py, viz.py, runs/.gitkeep}`
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A previous attempt existed; we'll redesign rather than resurrect, keeping
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only the lessons (parity-tested env, VecNormalize wrapper, eval cadence).
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---
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## 2. Design decisions
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### 2.1 Pen location — keep inside-field with N gate
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The user offered moving the pen *external* (through a wall hole). Tradeoffs:
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| Option | Pros | Cons |
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| **(A) Keep inside-field** (current) | World already built; Strömbom logic already tuned; gate corridor is short | Dog must navigate around three pen walls; adds geometric clutter |
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| (B) External pen via wall hole | Cleaner field — dog only sees sheep + outer walls; pen as goal region beyond a 3 m hole at y=−15 | Requires editing `field.wbt` (split south wall, add external pen walls beyond y<−15); existing rescue/deadzone logic must be retuned; outside-field flocking constants don't currently apply |
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**Recommendation: keep (A)** for parity with the working Strömbom controller,
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but add a **simplification**: widen the pen entrance from 3 m (x ∈ [10, 13])
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to 4 m (x ∈ [9.5, 13.5]) and raise the entrance line from y=−8 to y=−7.5
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to give the dog more turning room. Optional later: gate B as a curriculum
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extension (Section 7).
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### 2.2 Where to train
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PPO on Webots directly is too slow (real-time stepping, single env, slow
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reset). The previous training scaffolding used a Python 2D sim — that is
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the right approach. Constraints for sim-to-sim transfer:
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1. **Use the exact same flocking math**: import `controllers/sheep/flocking.py`
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from the env, do not reimplement.
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2. **Use the same world constants**: import `controllers/shepherd_dog/strombom.py`
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for pen geometry and Strömbom baseline.
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3. **Model differential drive faithfully**: match wheel-radius, base, and
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max wheel-velocity from the proto files. Heading update from
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`(ω_R − ω_L)·r / b`, position from `(ω_R + ω_L)·r / 2`.
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4. **Match Webots step**: `basicTimeStep = 16 ms`. The sheep controller runs
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at every basic step; the env will use the same `dt = 0.016 s`.
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5. **Lidar deferred**: dog policy will use a *symbolic* observation
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(positions of dog + sheep, plus pen geometry) — not raw lidar — for the
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first iteration. Lidar-from-pixels is a much harder learning problem
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and isn't required for the herding task. (See Section 7 for an
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optional later upgrade.)
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### 2.3 Action space for the dog
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Two viable choices:
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- **(a) High-level velocity vector** `(vx, vy) ∈ [−1, 1]²`. The same
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representation Strömbom emits today; the existing
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`drive_action(vx, vy, ...)` function in `shepherd_dog.py` converts this
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to wheel speeds. Decouples the policy from low-level diff-drive
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oscillations and enables direct A/B against Strömbom.
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- (b) Direct wheel speeds `(ω_L, ω_R) ∈ [−1, 1]²`. More expressive but the
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policy must learn diff-drive control from scratch — which is exactly
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the source of the wall-stuck and oscillation pain we're trying to
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avoid.
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**Recommendation: (a)** — high-level `(vx, vy)`. Reuses the well-tuned
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`drive_action` controller, which already handles `cos(err)` clamping and
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turn gain. RL focuses on *strategy*, not actuation.
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### 2.4 Observation space for the dog
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Symbolic, fixed-size, normalized to [−1, 1]:
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| Field | Dim | Notes |
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| Dog (x, y, cos h, sin h) | 4 | Position normalized by 15 |
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| Sheep CoM (x, y) | 2 | Of *active* (not-penned) sheep |
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| Sheep dispersion (radius, std-x, std-y) | 3 | Strömbom collect-vs-drive features |
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| Vector dog→CoM (dx, dy, dist) | 3 | Helps the value function |
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| Vector dog→pen-entry (dx, dy, dist) | 3 | |
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| Vector furthest-sheep→CoM (dx, dy) | 2 | Strömbom collect target hint |
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| Min sheep-to-wall distance + min dog-to-wall | 2 | Safety signal |
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| Active sheep count / N_max | 1 | |
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| 8-bin polar histogram of sheep around dog | 8 | Order-invariant flock shape |
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Total: **28 features**. Order-invariant by construction (histogram + summary
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stats), so the policy generalizes across flock sizes 1..N_max.
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### 2.5 Reward
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Sparse-only is too hard at flock scale; we shape conservatively.
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```
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r_t = w_pen · ΔN_penned # +1 per newly penned sheep
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+ w_progress· (d_CoM_pen[t-1] − d_CoM_pen[t]) # closer-to-pen progress
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+ w_compact· (R[t-1] − R[t]) # tighter flock progress
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− w_time · 1 # constant time penalty
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− w_wall · I(min_wall_dist < 1.0 m) # dog too close to wall
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− w_collide· I(dog within 0.3 m of any sheep) # avoid contact
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+ w_done · I(all sheep penned) # terminal bonus
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```
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Initial weights: `w_pen=2.0, w_progress=0.5, w_compact=0.2, w_time=0.005,
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w_wall=0.01, w_collide=0.05, w_done=10.0`. Tune via 1-sheep curriculum
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first — if the dog learns 1-sheep cleanly, the weights are sane.
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### 2.6 Episode
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- Max steps: 3000 (≈ 48 s at dt=16 ms — generous).
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- Termination: all sheep penned (success), dog/sheep stuck > 600 steps with
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no progress (failure), step limit (timeout).
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- Reset: domain-randomized — sheep count ∈ {1..N_max}, sheep positions
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uniform in field minus pen+gate corridor, dog at origin ± U(−2, 2).
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### 2.7 Curriculum
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| Stage | N_sheep | Duration (steps) | Pass criterion |
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| 0 | 1 | 0.5 M | success ≥ 90 % |
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| 1 | 2 | 1.0 M | success ≥ 80 % |
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| 2 | 3 | 1.5 M | success ≥ 70 % |
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| 3 | 1..3 mixed | 2.0 M | mean reward stable |
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| 4 (optional) | 5 | 2.0 M | success ≥ 60 % |
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Implemented by changing only `n_sheep` in the env reset.
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---
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## 3. Repository layout (new)
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```
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project/
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├── controllers/
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│ ├── sheep/ # unchanged
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│ ├── shepherd_dog/ # Strömbom controller (renamed entry)
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│ │ ├── shepherd_dog.py # mode-switch wrapper: RL | strombom
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│ │ ├── strombom.py # unchanged (canonical Strömbom)
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│ │ └── policy_loader.py # NEW: loads SB3 zip + VecNormalize
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│ └── ...
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├── herding/ # NEW: Python package, importable from env + controller
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│ ├── __init__.py
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│ ├── geometry.py # field/pen constants, in_pen(), wall helpers (single source of truth)
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│ ├── flocking_sim.py # vectorised numpy port of flocking.py for fast batched sheep
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│ ├── diffdrive.py # diff-drive integrator matching the proto specs
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│ └── obs.py # observation builder shared by env and Webots controller
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├── training/ # NEW
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│ ├── herding_env.py # gymnasium.Env, single-agent (the dog)
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│ ├── parity_test.py # asserts env trajectory ≈ Webots trajectory for fixed seeds
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│ ├── train_ppo.py # SB3 PPO entry point
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│ ├── eval.py # rollout + metrics (success rate, time-to-pen)
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│ ├── configs/
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│ │ ├── ppo_default.yaml
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│ │ └── curriculum.yaml
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│ ├── runs/ # tensorboard + checkpoints (.gitignored)
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│ └── requirements.txt
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├── docs/
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│ └── project.md # unchanged
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├── plan.md # this file
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└── ...
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```
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`herding/` becomes the **single source of truth** for geometry and dynamics.
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The Webots controllers and the training env both import from it, so when a
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constant changes in one place it changes everywhere — eliminating the
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sim/Webots-drift class of bugs.
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This means the existing `controllers/sheep/flocking.py` and
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`controllers/shepherd_dog/strombom.py` become thin shims that re-export
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from `herding/`. Webots controllers can import `herding/` because Webots
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adds the project root to `sys.path` at controller startup; we'll verify.
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---
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## 4. The Gymnasium environment — `training/herding_env.py`
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```python
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class HerdingEnv(gymnasium.Env):
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metadata = {"render_modes": ["rgb_array", "human"]}
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def __init__(self, n_sheep=3, max_steps=3000, dt=0.016, seed=None):
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self.action_space = Box(low=-1, high=1, shape=(2,), dtype=np.float32)
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self.observation_space = Box(low=-1, high=1, shape=(28,), dtype=np.float32)
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...
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def reset(self, *, seed=None, options=None):
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# Random sheep positions in field \ pen corridor, dog near origin.
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# Optional curriculum: options["n_sheep"] overrides.
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...
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def step(self, action):
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vx, vy = action # high-level velocity intent
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# Convert to wheel speeds via the same drive_action inverse used in Webots
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wL, wR = self._diffdrive_inverse(vx, vy, self.dog_state)
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self.dog_state = self._integrate_diffdrive(self.dog_state, wL, wR, self.dt)
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# Step every sheep one boid step (vectorized in flocking_sim.py)
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self.sheep_state = self._step_sheep(self.sheep_state, self.dog_state)
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# Update penned set, compute reward, observation, done flags
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...
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```
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Key points:
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- **Vectorised sheep update**: re-implements `flocking.py` in numpy so 100
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parallel envs with 5 sheep each take ms, not seconds. Numerical parity
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with the scalar version is asserted in `parity_test.py`.
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- **Same diff-drive integrator** for the dog as Webots will see at
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inference. Wall + pen-fence collisions clamp position (a Webots-realistic
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no-pass-through approximation).
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- **Domain randomization** in reset: sheep count, spawn positions, sheep
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flock-parameter jitter (±10 % on FLEE_DIST, COHESION_DIST, etc.) for
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robustness.
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---
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## 5. Training pipeline — `training/train_ppo.py`
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- **Algorithm**: SB3 `PPO` with `MlpPolicy`, `n_steps=2048`, `batch_size=256`,
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`n_epochs=10`, `gamma=0.995`, `gae_lambda=0.95`, `clip_range=0.2`,
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`ent_coef=0.005`, `vf_coef=0.5`, `learning_rate=3e-4`.
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- **Vec envs**: `SubprocVecEnv` × 16 parallel envs (the env is pure numpy
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so subprocs are CPU-cheap).
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- **Normalization**: `VecNormalize(norm_obs=True, norm_reward=True,
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clip_obs=10.0)`. Pickled alongside the policy zip — both required at
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inference.
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- **Callbacks**:
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- `CheckpointCallback` every 100 k steps.
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- `EvalCallback` on a separate eval env (no normalization-update) every
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50 k steps; logs success rate and time-to-pen to TensorBoard.
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- Custom `CurriculumCallback`: bumps `n_sheep` when eval success rate
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crosses the stage threshold for 3 consecutive evals.
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- **Determinism for debugging**: seed-pinned eval env so regressions are
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catchable.
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---
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## 6. Webots integration — RL inference path
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`controllers/shepherd_dog/shepherd_dog.py` becomes a thin wrapper:
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```python
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MODE = os.environ.get("HERDING_MODE", "rl") # "rl" | "strombom"
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if MODE == "rl":
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policy = policy_loader.load("training/runs/best/policy.zip",
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"training/runs/best/vecnormalize.pkl")
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obs_fn = build_obs # from herding/obs.py
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else:
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obs_fn = None # strombom path uses sheep_positions directly
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while robot.step(timestep) != -1:
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receive_messages()
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if MODE == "rl":
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obs = obs_fn(dog_xy, dog_heading, sheep_positions, ...)
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action, _ = policy.predict(obs, deterministic=True)
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vx, vy = action.tolist()
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else:
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vx, vy, mode, dbg = compute_action_debug(dog_xy, sheep_positions, PEN_ENTRY)
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# plus existing rescue/cooldown/EMA layer
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drive_action(vx, vy, ...)
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```
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A **safety supervisor** wraps the RL output: if `obs` indicates the dog is
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< 0.6 m from a wall, override with the existing wall-escape behavior
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(reverse + turn). This is a hard guarantee diff-drive needs because PPO
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may not discover wall-escape reliably from on-policy data.
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`policy_loader.py` handles the SB3 import lazily so the controller still
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works with `MODE=strombom` even if SB3 is not installed in the Webots
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Python environment.
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---
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## 7. Optional extensions (post-baseline)
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- **External pen** (Section 2.1 option B): edit `field.wbt` to extend the
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south wall hole into an external L-shaped pen with its own walls; update
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`herding/geometry.py`; retrain stage 3 only.
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- **Lidar observation**: replace symbolic obs with 36-bin downsampled
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lidar + ego state; train end-to-end. Useful as the "extra merit"
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dimension in the project doc.
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- **Two-dog mode**: make env multi-agent, train with `MAPPO`-style shared
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critic or independent PPO. The proto already supports multiple dog
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instances; world only needs a second `ShepherdDog` node.
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- **Mecanum comparison**: swap the dog proto for a mecanum variant; same
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policy, different `_integrate_diffdrive` (becomes holonomic).
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- **Sheep flock size scaling**: 5, 10, 20 — the obs is order-invariant so
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the same policy generalises; just curriculum further.
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---
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## 8. Risks & mitigations
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| Risk | Mitigation |
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|---|---|
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| Sim-to-Webots gap (sheep dynamics, wall friction) | `parity_test.py` asserts trajectory match within tolerance for fixed seeds; if it fails, fix the env, not the policy |
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| Dog learns to wall-pin sheep against fence | Add `w_collide` penalty + min-sheep-to-wall term in obs; curriculum from 1 sheep first |
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| PPO oscillation collapses into spinning | Action smoothing in env step (EMA on `(vx, vy)`, mirroring `ACTION_SMOOTH=0.35` from Strömbom controller); reward small `‖a_t − a_{t-1}‖` penalty |
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| Pen approach failures (sheep refuse gate) | Reuse the existing `deadzone_rescue` as a *scripted fallback* triggered when a sheep has been deadzoned > 200 steps — RL handles the common case, scripted handles the corner |
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| Gym version mismatch (gymnasium vs gym) | Lock to `gymnasium>=0.29`, `stable-baselines3>=2.3` in requirements |
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---
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## 9. Milestones (suggested order of implementation)
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1. **M0 — Refactor** (no behavior change): create `herding/` package, move
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constants out of `flocking.py`/`strombom.py`, leave shims; verify
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Webots still runs Strömbom unchanged. Add `dog_behavior_log.csv` to
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`.gitignore`.
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2. **M1 — Env & parity**: `herding_env.py`, `parity_test.py`. Asserts
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sheep + dog trajectories match Webots within tolerance for 5 fixed
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seeds. *Done when parity test green.*
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3. **M2 — PPO baseline**: train Stage 0 (1 sheep) for 0.5 M steps; eval
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in env at ≥ 90 % success.
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4. **M3 — Webots inference**: load Stage 0 policy in `shepherd_dog.py`
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with `HERDING_MODE=rl`; verify the dog herds 1 sheep into the pen in
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the actual Webots world. *This is the sim-to-sim transfer gate.*
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5. **M4 — Curriculum**: stages 1–3, ~5 M steps total, with checkpoints
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and eval logs.
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6. **M5 — Strömbom comparison**: run both controllers on a fixed eval
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suite (same seeds, 1/2/3 sheep), log success rate and time-to-pen.
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This is a deliverable for the project's "quantitative evaluation"
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goal.
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7. **M6 — Documentation**: a short README in `training/` showing how to
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train, evaluate, and switch modes in Webots.
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Each milestone is independently demoable. M0–M3 is the critical path to
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"RL works in Webots"; M4–M6 polishes it for the project deliverable.
|
||
|
||
---
|
||
|
||
## 10. Decisions (locked in by implementation)
|
||
|
||
- **Pen layout**: option B (external pen). The pen sits south of the
|
||
field at x ∈ [10, 13], y ∈ [-22, -15] and is reached through the
|
||
existing 3 m gap in the south stone wall. The old in-field
|
||
quarantine fence is gone and the wooden gate is modeled as
|
||
swung-open and parked on the west gate post so the corridor is
|
||
unobstructed. This kills the deadzone class entirely.
|
||
- **Flock size**: 1..10 sheep, sampled uniformly each reset. The order-
|
||
invariant observation (CoM, dispersion, polar histogram) lets a
|
||
single policy generalise across the whole range. A curriculum widens
|
||
``max_n_sheep`` from 1 to 10 over training to keep early exploration
|
||
tractable.
|
||
- **Single-sheep mode**: handled by the same policy (n_sheep=1 is the
|
||
first stage of the curriculum and stays in the training distribution
|
||
throughout). No separate model.
|
||
- **Hardware**: GPU for training. SubprocVecEnv × 16 on CPU feeds an
|
||
MlpPolicy on GPU; ~2–3 h for the full curriculum.
|
||
|
||
## 11. What was built
|
||
|
||
```
|
||
herding/ # single source of truth, importable from both
|
||
geometry.py # field/pen constants, latch helpers, robot specs
|
||
flocking_sim.py # Reynolds boid step (matches Webots controller)
|
||
diffdrive.py # diff-drive kinematics + velocity↔wheels
|
||
obs.py # 28-D order-invariant observation builder
|
||
strombom.py # collect/drive heuristic (baseline + fallback)
|
||
|
||
worlds/field.wbt # external pen south of field, 10 sheep slots,
|
||
# gate parked open, in-field fence removed
|
||
|
||
controllers/sheep/sheep.py # imports from herding/, latches on
|
||
# is_penned_position
|
||
controllers/shepherd_dog/
|
||
shepherd_dog.py # mode switch (HERDING_MODE=rl|strombom),
|
||
# safety supervisor for DOG_SOUTH_LIMIT
|
||
policy_loader.py # lazy SB3 zip + VecNormalize loader
|
||
strombom.py # shim re-exporting herding.strombom
|
||
|
||
training/
|
||
herding_env.py # gymnasium.Env, action smoothing, reward shaping
|
||
train_ppo.py # SB3 PPO with VecNormalize, eval, checkpoints,
|
||
# curriculum callback
|
||
eval.py # success-rate / time-to-pen across n_sheep
|
||
parity_test.py # shape, determinism, baseline-rollout smoke test
|
||
configs/ppo_default.yaml
|
||
requirements.txt
|
||
README.md # how to train, evaluate, switch modes in Webots
|
||
```
|
||
|
||
## 12. To run
|
||
|
||
```bash
|
||
# 1. Install deps (CUDA-enabled torch wheel for GPU)
|
||
pip install -r training/requirements.txt
|
||
|
||
# 2. Smoke test
|
||
python -m training.parity_test
|
||
|
||
# 3. Train (5 M steps, ~2–3 h on a single GPU)
|
||
python -m training.train_ppo --out-dir training/runs/baseline
|
||
|
||
# 4. Evaluate vs Strömbom
|
||
python -m training.eval --policy training/runs/baseline/best
|
||
python -m training.eval --policy strombom
|
||
|
||
# 5. Run in Webots
|
||
export HERDING_MODE=rl
|
||
export HERDING_POLICY_DIR=$PWD/training/runs/baseline/best
|
||
webots worlds/field.wbt
|
||
```
|