Sampling-Aware Capacity-Feedback Control for Safety-Margin Recovery in Bimanual Robot Grasp-and-Cut Tasks

In bimanual grasp-and-cut tasks, the safety distance between arms must be temporarily reduced for close-proximity manipulation and restored after task completion. However, fixed recovery rates or rapid changes in the safety distance can make the recovery infeasible under joint, collision, grasp, and command constraints. To address this issue, we propose a capacity-feedback control framework that adjusts safety-margin recovery according to the robot’s currently feasible control inputs. At each control cycle, a linear program (LP) computes the maximum safety-distance recovery rate supported by the admissible input set. The requested recovery rate is then determined from this capacity with a capacity-scaling factor selected using a sampling-aware tuning rule to reduce the sensitivity of the recovery response to the control period. A subsequent motion-control QP computes the joint-velocity command while satisfying the selected recovery demand and task constraints. Numerical simulations evaluate the recovery behavior and point-wise feasibility of the proposed method under several constraint conditions and compare it with alternative recovery strategies, including fixed-rate recovery. The capacity-feedback behavior is further evaluated in closed-loop rigid-body simulations using Isaac Sim. Sampling-aware tuning reduced the maximum relative recovery-time deviation from 296.634% to 7.143%, while the proposed method successfully completed all 20 physical-robot recovery trials across four task configurations.