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新论文 · v1arXiv:2611.10877

Sampling-based Model Predictive Control as Sequential Monte Carlo Inference

Austin Garrett, Muyang Yan, Zachary Kingston

原文摘要

Sampling-based model predictive control (MPC) is treated as a family of distinct algorithms: predictive sampling, MPPI, the cross-entropy method, and their biased, colored-noise, and annealed variants all sample control sequences and reweight them by cost. We show a single Sequential Monte Carlo (SMC) inference algorithm implements them all, specified by three independent axes: the proposal, the update rule that reduces cost-weighted particles to a nominal plan, and the effective-sample-size threshold for resampling. We evaluate SMC-based planning on MuJoCo non-linear control, open-loop navigation through obstacle fields, 7-DoF motion planning, and non-prehensile push-T, in simulation and on a Franka FR3. Resampling combats particle degeneracy at long horizons. At a 28-knot horizon in our navigation domain it cut the collision rate by 30% over non-resampling baselines. Programmable proposals improve push-T success over perturbative baselines, improving from 1% to 100% in simulation and 10% to 90% on hardware.

cs.RO · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.19610

Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots

Qingxi Meng, Emiliano Flores, Carlos Quintero-Peña, Peizhu Qian, Zachary Kingston, Shannan K. Hamlin, Vaibhav Unhelkar, Lydia E. Kavraki

原文摘要

Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad range of robot perception objectives, including object detection, human activity recognition, and human face detection. For example, a service robot may need to continuously localize an object during manipulation, while an assistive robot may need to reliably perceive a human face or activity for interaction and safety. These tasks impose perception constraints on robot motion. However, solving motion and perception tasks simultaneously is challenging, as their requirements often conflict. Furthermore, robots must react quickly to environmental changes, while directly evaluating perception quality (e.g., object detection confidence) is often expensive or infeasible at runtime. This problem is especially important in human-centered environments, such as homes and hospitals, where effective perception is essential for safe operation. In this work, we address motion planning for high-degree-of-freedom (DoF) robots from a start to a goal configuration with continuous perception constraints in static and dynamic environments. Our solution is a GPU-parallelized perception-score-guided probabilistic roadmap planner with a neural surrogate model (PS-PRM). Unlike existing active perception-, visibility-aware, or learning-based planners, our work jointly considers perception tasks and constraints when searching for a motion-planning solution. Our method uses a neural surrogate model to approximate perception scores, incorporates them into roadmap planning, and leverages GPU parallelism for efficient online replanning. We demonstrate that our planner outperforms RL- and trajectory-optimization-based baselines in static and dynamic environments in simulation and real-robot experiments.

cs.RO

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arXiv ↗PDF ↗