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    "headline": "The Collision Model Learned Where False Safety Hurts",
    "slug": "the-collision-model-learned-where-false-safety-hurts",
    "dek": "A differentiable distance field puts extra training weight on sign errors near the boundary between clearance and contact.",
    "summary": "A differentiable distance field puts extra training weight on sign errors near the boundary between clearance and contact.",
    "body_text": "PI-UDF combines analytical forward kinematics, learned link-geometry embeddings and a shared residual network to predict distances between articulated robot bodies. Its training procedure mines samples near the collision boundary and penalizes false-safe sign errors asymmetrically. The resulting field becomes a differentiable clearance term inside nonlinear model-predictive control. Tests included a real dual-Franka platform performing close-proximity 14-degree-of-freedom arm swaps and dynamic evasion, plus offline replay against established geometry tools. The paper presents evidence that the estimator is useful for closed-loop planning; it does not replace certified collision checking or prove safety outside the tested configurations.",
    "why_it_matters": "A differentiable distance field puts extra training weight on sign errors near the boundary between clearance and contact.",
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      "The paper presents evidence that the estimator is useful for closed-loop planning; it does not replace certified collision checking or prove safety outside the tested configurations."
    ],
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    "tags": [
      "collision avoidance",
      "robot control",
      "differentiable geometry"
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      "title": "arXiv preprint 2610.12404, original v1",
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      "published_at": "2026-10-08T13:45:04.000-04:00",
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    "title": "The Collision Model Learned Where False Safety Hurts",
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