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physicalai-bmi/efa-2-acrobot
efa-2-acrobot is a robotics model from physicalai-bmi. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for ferric. The card lists the license as apache-2.0.
The first EFA body where the energy gate doesn't just price compute — it rescues. Gym Acrobot-v1, exact published spec: underactuated (torque on the elbow only, actions {−1, 0, +1}), RK4 dynamics, reward −1/step, 500-…
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From the Hugging Face model README
The first EFA body where the energy gate doesn't just price compute — it rescues. Gym Acrobot-v1, exact published spec: underactuated (torque on the elbow only, actions {−1, 0, +1}), RK4 dynamics, reward −1/step, 500-step cap. Nothing about the task is ours.
Charlot Lab · Institute for Physical AI @ Bailey Military Institute. Runtime: Ferric. Siblings: efa-1 (flagship, certified multi-body) · efa-2-pendulum.
From the hanging start, the optimal pumping torque is symmetric-bimodal (±1) — and a flow policy's conditional
expectation at a₀ = 0 averages the modes toward zero. Measured on this artifact: K=1 rounds to zero torque on 54%
of hanging-region probes. That is the classically multi-modal regime where one-shot policies fail — and it shows in
the closed loop.
| policy | mean return | solved | extra compute |
|---|---|---|---|
| flow K=1 (no gate) | −109.9 | 96% | — |
| AGENCY: E>τ → K=4 → planner | −88.0 | 100% | on 5.3% of decisions |
| flow K=4 always | −80.9 | 100% | on 100% of decisions (4×) |
| planner tool always (argmin own E) | −84.8 | 100% | 3 E-evals every step |
| DP teacher (near-optimal) | −84.7 | 100% | 106 evals/step |
The model's own energy objects on ~5% of decisions, and that objection converts into solved episodes: +21.9 mean
return, 96→100% solved, capturing most of always-K=4's gain at a small fraction of its marginal compute. The same
potential verifies at 94.5% (ranks the demonstrator's action below the alternatives); every path — including both
tools — is bit-exact deterministic. τ ships in config.json (95th pct of validation energy, from the artifact alone).
config.json).experiments/ebm_efa2acro.rs in the EFA repo
(ledger with negatives included — on two easier bodies the gate found nothing to rescue, and the cards say so).Coordinated pair on the env's own observation [cosθ₁, sinθ₁, cosθ₂, sinθ₂, ω₁/4π, ω₂/9π]:
flow head (8→96→96→1, CFM; K-step integration, rounded to the 3 legal actions) + potential head (7→96→96→1,
contrastive; the verifier, the gate, and the planner tool's objective — one energy, three roles).
License: Apache-2.0.