FARO: Feasibility-Aware Robot Motion Optimization
This paper introduces FARO, a framework for rapid planning of novel behaviors in unseen scenarios for humanoid loco-manipulation. It integrates a nested kino-dynamic feasibility checker, LLM-based contact sampling, and an RL controller to improve search efficiency and generate high-quality, executable trajectories.
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[Submitted on 20 Jul 2026]
Title:FARO: Feasibility-Aware Robot Motion Optimization
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Abstract:Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulation continues to hinder the realization of this goal. In this paper, we address this challenge by proposing a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation given a candidate contact sequence. By integrating this module with a feasibility-guided tree search and a Large Language Model (LLM)-based contact plan sampling strategy, we demonstrate that the proposed framework can substantially improve the search process. Furthermore, we show that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller and show that the resulting trajectories are of sufficiently high quality for execution in real-world loco-manipulation scenarios. A supplementary video is available at: this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.18362 [cs.RO]
(or arXiv:2607.18362v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18362
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Michal Ciebielski [view email] [v1] Mon, 20 Jul 2026 14:22:41 UTC (5,329 KB)
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