[2605.16398] Support-Safe Variational Hybrid Filtering for Contact-Mode and Sparse-Law Recovery
[Submitted on 12 May 2026]
Title:Support-Safe Variational Hybrid Filtering for Contact-Mode and Sparse-Law Recovery
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Abstract:Contact-rich robot dynamics are hybrid: a single observation can match several latent states and contact regimes (free, impact, stick--slip). A standard amortized filter that places no probability on a feasible contact transition will permanently lose the branch the robot actually follows. We introduce VHYDRO, a variational hybrid dynamics learner that prevents this branch loss. At each step, VHYDRO mixes the learned proposal with a feasible transition law before sampling and importance weighting, ensuring that every transition retained by the model-feasible carrier remains covered. VHYDRO jointly infers a continuous latent state and a discrete contact mode, and fits a sparse port-Hamiltonian law to each recovered regime. On top of this, three guarantees connect: support coverage stabilizes filtering, the stabilized filter concentrates the discrete contact posterior on coherent regimes, and mode-pure segments admit sparse port-Hamiltonian recovery. The recovery error separates cleanly into filtering, derivative, mode-impurity, and physics-residual parts. Three empirical findings track the same mechanism. Under heavy occlusion the support-safe filter stays usable while a non-defensive proposal collapses. On ManiSkill demonstrations and on four Sawyer/BridgeData task families the discrete state forms temporally coherent contact-regime segments that the discrete state yields a stronger joint profile across ARI, change-point F1, and segment purity than post-hoc and mode-free baselines. On hybrid systems with known equations the mode-conditioned sparse fit recovers the active physical terms; purely predictive baselines do not.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.16398 [cs.RO]
(or arXiv:2605.16398v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.16398
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Marios Papamichalis Dr [view email] [v1] Tue, 12 May 2026 18:13:35 UTC (4,489 KB)
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