发表机构
Istituto Italiano di Tecnologia; University of Genova（意大利理工学院; 热那亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动机械臂操作中力能力与灵巧性难以兼顾的问题，提出基于VLM推断任务力需求、以任务力不确定性球与动态剩余力多面体符号距离为度量的多目标轨迹优化框架，实现重载时保力、轻载时保灵巧的自适应平衡。
AI 中文摘要
在机器人操作中，有效的物理交互控制不仅需要运动学上可行的运动，还需要足够的力交互能力。现有的冗余度求解方法往往忽略特定任务的力需求，或不加区分地最大化力能力，在不需要较大力裕度时牺牲了灵巧性。我们提出了一种面向任务的冗余移动机械臂力能力优化框架。视觉语言模型（VLM）从RGB图像和任务描述中推断物体物理属性，生成一个捕获重力和惯性需求的目标任务力序列。然后，我们将面向任务的力能力度量定义为任务力不确定性球与动态剩余力多面体（RFP）之间的符号距离，量化任务需求与机器人剩余驱动能力之间的兼容性。该度量与可操作性、关节极限规避、轨迹平滑性和基座振荡抑制一起，被纳入全身多目标轨迹优化问题中。在移动机械臂执行提升和单点保持任务、且负载条件变化的情况下进行的实验表明，所提方法为重负载提供了足够的力能力，同时为轻负载保持了较高的可操作性。这实现了任务自适应的平衡，而固定的能力最大化基线（RFP内切半径、RFP锥）和仅可操作性优化均无法实现。核心实现已公开于该https URL。
英文摘要
Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction capability. Existing redundancy resolution methods often ignore task-specific force demands or maximize the force capability indiscriminately, sacrificing dexterity when large force margins are unnecessary. We propose a task-oriented force capability optimization framework for redundant mobile manipulators. A Vision-Language Model (VLM) infers object physical properties from an RGB image and a task description, generating a desired task-force sequence that captures gravitational and inertial demands. We then define a task-oriented force capability metric as the signed distance between a task-force uncertainty ball and the dynamic residual force polytope (RFP), quantifying compatibility between task demands and the robot's remaining actuation capacity. This metric is incorporated, alongside manipulability, joint-limit avoidance, trajectory smoothness, and base-oscillation suppression, into a whole-body multi-objective trajectory-optimization problem. Experiments on a mobile manipulator performing lifting and single-point-holding tasks under varying payload conditions demonstrate that the proposed method provides sufficient force capability for heavy loads while preserving high manipulability for light loads. This yields a task-adaptive balance that fixed capability-maximizing baselines (RFP inscribed radius, RFP cone) and manipulability-only optimization fail to achieve. The core implementation is publicly available at https://github.com/yeying256/FORTE.
