提出新框架，让机器人快速规划复杂动作并验证可行性。
FARO: Feasibility-Aware Robot Motion Optimization
- 嵌套运动学动力学框架，快速判断动作可行性
- 结合大模型采样与搜索，显著提升规划效率
- 生成轨迹可被强化学习控制器跟踪，适合真实场景
在未见过的场景中快速规划新行为仍是机器人领域的核心挑战。人形机器人在运动与操作中的高维、混合与欠驱动特性，持续阻碍这一目标的实现。本文提出一种嵌套式运动学-动力学框架，可在给定候选接触序列下快速进行可行性检查并生成动态一致的轨迹。将该模块与可行性引导的树搜索及基于大语言模型（LLM）的接触计划采样策略结合，显著优化了搜索过程。此外，实验表明生成的轨迹可通过强化学习（RL）控制器进行跟踪，且轨迹质量足以在真实世界的人形运动操作任务中执行。补充视频见：https://youtu.be/R6qCHoCormQ。
原文摘要 · Abstract (English)
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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