Multi-Objective Motion Planning

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Multi-Robot Multi-Goal Motion Planning with Stochastic Skills
Oct 6, 2026William Schnyder, Valentin N. Hartmann, Stelian CorosMulti-Robot Motion Planning Motion Planning
Computational Robotics Lab (CRL), ETH Zürich
M $^3$ P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation
Sep 16, 2026Xingpeng Sun, Zherong Pan, Kai Cheng +3Constrained Motion Planning Large Language Model-Based Robot Planning
Purdue University United States · LightSpeed Studios United States
PccDiffuser: Multi-solution Motion Planning for Continuum Robots
Sep 9, 2026Ke Qiu, Sifan Chen, Si Wang +3Constrained Motion Planning Diffusion-Based Planning
Department of Control Science and Engineering, Zhejiang University, Hangzhou, China
Stochastic Multi-Objective Kinodynamic Planning Against Adversaries
Jul 21, 2026Thomas Marshall Vielmetti, Daniel Cherenson, Dimitra PanagouConstrained Motion Planning Kinodynamic Motion Planning
Globalized Constrained Stein Variational Inference for Diverse Feasible Robot Motion Planning
Jul 14, 2026Jiayun Li, Georgia ChalvatzakiConstrained Motion Planning Constrained Optimization
PEARL Lab, Dept. of Computer Science, TU Darmstadt. · Hessian.AI, Darmstadt, Germany. · Robotics Institute Germany (RIG).
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Jul 1, 2026Qingyi Chen, Zachary Kingston, Ahmed H. QureshiMulti-Robot Motion Planning Constrained Motion Planning
Department of Computer Science at Purdue University, West Lafayette, IN, USA
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Jun 15, 2026Youngjae Min, Jovin D'sa, Faizan M. Tariq +3Hierarchical RL Motion Planning
1Massachusetts Institute of Technology, Cambridge, MA 02139. · 2Honda Research Institute, USA, San Jose, CA, 95134.
MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation
May 8, 2026Shih-Yu LaiV2X Communication Safe Motion Planning
National Taiwan University Taipei, Taiwan

Oct 6, 2026, cs.ROJ/K move · Enter open · S save

Multi-Robot Multi-Goal Motion Planning with Stochastic Skills

William Schnyder, Valentin N. Hartmann, Stelian Coros

Computational Robotics Lab (CRL), ETH Zürich

Multi-Robot Motion Planning Motion Planning Multi-Objective Motion Planning Sampling-Based Motion Planning

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As robots are increasingly deployed in groups and share workspaces to execute real-world tasks, planning their concurrent motions around complex manipulation skills becomes essential. These skills involve continuous physical execution and may exhibit stochastic behavior, resulting in variable execution times and uncertain continuous trajectories. Existing planners either limit execution to single-robot scenarios, rely on open-loop paths, or use post-hoc scheduling that prevents dynamic coordination. In this paper, we address this gap by integrating stochastic skills into sampling-based multi-robot planning by formulating the problem as a Markov Decision Process (MDP) over a multi-modal composite roadmap. For stochastic skills, solving the MDP yields a reactive policy that allows controllable robots to dynamically adapt their motions in response to other robots' execution of manipulation skills. By resolving skill uncertainty directly at planning time, this approach avoids the pessimism of conservative baselines and unlocks robust, dynamic multi-robot coordination. Code for the planners is available at https://www.vhartmann.com/stochastic-skills.

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