[2605.07215] PISTO: Proximal Inference for Stochastic Trajectory Optimization
[Submitted on 8 May 2026]
Title:PISTO: Proximal Inference for Stochastic Trajectory Optimization
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Abstract:Stochastic trajectory optimization methods like STOMP enable planning with non-differentiable costs, offering substantial flexibility over gradient-based approaches. We show that STOMP implicitly minimizes the KL divergence from a Boltzmann trajectory distribution, revealing an elegant Variational Inference (VI) structure underlying its updates. Building on this insight, we propose the \textit{Proximal Inference for Stochastic Trajectory Optimization} (PISTO) algorithm that stabilizes the updates by augmenting the objective with a KL regularization between successive Gaussian proposals. This proximal formulation admits a trust-region interpretation and yields closed-form mean updates computable as expectations under a surrogate distribution. We estimate these expectations via importance-weighted Monte Carlo sampling, producing a simple, derivative-free algorithm that inherits STOMP's ability to handle non-differentiable and discontinuous costs without modification. On robot arm motion planning benchmarks, PISTO achieves an 89\% success rate -- outperforming CHOMP (63\%) and STOMP (68\%) -- while producing shorter, smoother paths at twice the speed of competing stochastic methods. We further validate PISTO on contact-rich MuJoCo locomotion and manipulation tasks, where it consistently outperforms both CEM and MPPI baselines in reward.
Comments: 8 pages
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.07215 [cs.RO]
(or arXiv:2605.07215v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.07215
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hongzhe Yu [view email] [v1] Fri, 8 May 2026 04:04:30 UTC (9,109 KB)
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