发表机构
U.S. Naval Research Laboratory（美国海军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种仅用关节角与腕部力/力矩传感器的在线自适应方案，推导了稳定的运动学更新律，通过仿真验证其在孔轴插入任务中的有效性，为操作学习分解为任务策略与自适应运动学组件奠定基础。
AI 中文摘要
接触丰富的机器人操作需要机器人关节与所感知任务特征之间运动学关系的精确模型。该关系极少被精确知晓：它会随机器人拾取的每个工具发生变化，且当接触模式改变时（尤其是全手抓取这类多指尖接触点未精确规定的多指手情况），有时几乎会瞬间变化。本文提出一种自适应方案，仅使用关节角传感和腕部安装的力/力矩传感器在线估计该关系，无需对工具末端进行外部感知测量。我们推导了可证明稳定的运动学更新律，该更新律仅从力/力矩反馈中识别未知工具的运动学特性，并证明了刚性情况及将柔顺控制器作为内环的情况的稳定性。我们表明，识别仅局限于运动激发的方向——例如，在刚性插入推动下，工具长度不可观测，而柔顺回路的被动屈服会部分激发工具长度；且采用二阶导纳时，柔顺性保证在连续时间中无条件成立。我们还将组合的控制与估计问题表述为二次规划（QP）：该公式精确产生更新律的预测项，但有趣的是无法复现跟踪自适应项。我们在仿真中通过孔轴插入任务验证了该方案。本工作是一项研究计划的第一步，该计划旨在将操作学习分解为可独立于机器人学习的任务策略（例如通过强化学习）和在线适应当前使用的特定机器人、手或工具的自适应运动学组件。
英文摘要
Contact-rich robotic manipulation requires an accurate model of the kinematic relationship between a robot's joints and the task features it senses. This relationship is rarely known exactly: it changes with each tool the robot picks up and shifts, sometimes almost instantaneously, as contact modes change --- especially for multi-fingered hands that make and break contact at points that are not exactly prescribed, as in full-hand grasping. This paper develops an adaptive scheme that estimates that relationship online, using only joint-angle sensing and a wrist-mounted force/torque sensor, with no exteroceptive measurement of the tool tip. We derive a provably stable kinematic update law that identifies the kinematics of an unknown tool from force/torque feedback alone, and prove stability of both the rigid case and the case with a compliance controller as an inner loop. We show that identification is confined to the directions the motion excites --- so that, for example, a tool's length is unobservable under a rigid insertion push, while a compliant loop's passive yielding partially excites it; and that with a second-order admittance the compliant certificate holds unconditionally in continuous time. We also pose the combined control and estimation problem as a Quadratic Program (QP): the formulation yields the prediction term of the update law exactly but, instructively, cannot reproduce the tracking adaptation term. We validate the scheme in simulation on a peg-in-hole insertion. This work is the first step in a research program aimed at factoring manipulation learning into a task policy which can be learned in isolation of the robot, for instance by reinforcement learning, and an adaptive kinematic component that adapts online to the particular robot, hand, or tool in use.
Comments29 pages including appendices, 3 figures
