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arXiv · 2607.18362

FARO: Feasibility-Aware Robot Motion Optimization

Michal Ciebielski·Shafeef Omar·Aaron Johnson·Majid Khadiv
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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: https://youtu.be/R6qCHoCormQ.

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Michal Ciebielski, Shafeef Omar, Aaron Johnson, Majid Khadiv. 2026-07-20. FARO: Feasibility-Aware Robot Motion Optimization. https://arxiv.org/abs/2607.18362

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