Johnny Fernandes
27c0f65722
Replace the failing ODE-rolled mecanum chassis dynamics with a Supervisor.setVelocity call that uses the gym mecanum forward kinematics formula directly. Wheel motors still spin (visual); chassis motion comes from the gym model so training and deployment match by construction. Results (seed=42, n=10 sheep): BC + RL mecanum pen 10/10 in both field and field_round. n=5 mecanum cells still 0/5 due to tracker phantoms anchored to wall corners under the 360° LiDAR — documented in docs/status.md as the remaining gap. Cleanup: drop deploy-time hacks (HERDING_HEADING_*, HERDING_OMEGA_CLAMP, HERDING_TRACKER_*) that were workarounds for the old ODE chaos; revert the proto inertiaMatrix, roller dampingConstant, and reduced motor torque since they no longer carry load; refresh comments around the mecanum config presets.
36 lines
819 B
Plaintext
36 lines
819 B
Plaintext
# Python
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__pycache__/
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*.pyc
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.pytest_cache/
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# Training artefacts: ignore all run outputs except deployable policies
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training/runs/**
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!training/runs/
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!training/runs/.gitkeep
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!training/runs/*/
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!training/runs/*/policy.zip
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# BC demo blobs — these get regenerated by `python -m training.bc.collect`
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# and are too large to track. Keep them out of git.
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training/bc/*.npz
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training/bc/v1/
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# Webots launcher scratch (the _test.wbt files are emitted on every run)
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worlds/**
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!worlds/field.wbt
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!worlds/field_round.wbt
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herding_runtime.cfg
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# Runtime logs — all of these are produced by training/eval/webots runs
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# and are not useful to track in git. Keep summary docs/markdown only.
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*.log
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*.stdout
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calibrate_mecanum.log
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training/.run_done
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# Local-only training backups (never committed).
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_backup_pretrain/
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# Tooling
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.claude/
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