FARO: Feasibility-Aware Robot Motion Optimization

• URL: http://arxiv.org/abs/2607.18362v1
• Date: Mon, 20 Jul 2026 14:22:41 GMT
• Title: FARO: Feasibility-Aware Robot Motion Optimization
• Abstract summary: We propose a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation.<n>We show that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller.
• Score: 5.920538327471107
• License: http://creativecommons.org/licenses/by/4.0/
• 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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