发表机构
George Mason University; University of Florida（乔治梅森大学; 佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出能力权衡接触选择（CTCS）方法，用于腿式移动操作中联合选择支撑接触与全身配置，通过预测能力并加速优化，在仿真和硬件实验中优于固定接触方案。
AI 中文摘要
本文研究了在规定的移动操作任务中，环境支撑接触与全身配置的联合选择问题。接触可能提供更强的物理支撑，但同时会限制任务所需的运动。我们通过三个能力度量来表述该问题：满足任务要求后剩余的残余力螺旋、末端执行器可达范围以及基座移动性，并在这些能力与接触获取成本之间进行权衡。对每个候选接触点评估这些能力需要重复进行全身优化。为降低计算成本，我们提出了能力权衡接触选择（CTCS）方法。CTCS筛选候选接触点以确定其接触与任务可行性，在每个表面内对相似候选进行分组，并通过局部敏感性分析从精确锚点评估中预测其能力。它通过选择性的精确评估来检验这些预测，按能力对候选进行排序，并对最终选择进行精确评估。我们在仿真和硬件实验中，使用配备AgileX NERO机械臂的Unitree Go2四足机器人，在392种任务条件和九个可用支撑表面下对CTCS进行了评估。结果表明，CTCS优于仅地面支撑和固定接触支撑，因为它能选择为任务提供有利能力权衡的支撑表面。与对每个候选进行精确评估相比，CTCS实现了约3倍的加速，同时紧密匹配最终的平均目标值。
英文摘要
In this paper, we study the joint selection of an environmental support contact and a whole-body configuration for a prescribed loco-manipulation task. A contact may provide greater physical support while restricting the motion required for the task. We formulate this problem through three capability measures: residual wrench, end-effector reach, and base mobility available after satisfying the task requirements, and we balance them against contact acquisition cost. Evaluating these capabilities for every candidate requires repeated whole-body optimizations. To reduce this computational cost, we propose Capability-Tradeoff Contact Selection (CTCS). CTCS screens candidates for contact and task feasibility, groups similar candidates within each surface, and predicts their capabilities from exact anchor evaluations using local sensitivity analysis. It checks these predictions through selective exact evaluations, ranks candidates by capability, and evaluates a shortlist exactly for final selection. We evaluate CTCS in simulations and hardware experiments using a Unitree Go2 quadruped with an AgileX NERO arm across $392$ task conditions with nine available support surfaces. Results show that CTCS outperforms ground-only and fixed-contact support, as it can select support surfaces that provide favorable capability trade-offs for the task. Compared with evaluating every candidate exactly, CTCS achieves approximately $3\times$ speedup while closely matching the resulting mean objective value.
Comments9 pages, 6 figures
