Actuator-Aware Inverse Kinematics with Joint-Limit Admissibility for Torque-Controlled Redundant Robots

2026-05-29

AI Summary

This work addresses the challenge of degraded task performance in torque-controlled redundant robots under joint limit constraints, where conventional inverse kinematics formulations often fail to ensure feasibility and compatibility with downstream torque controllers. To overcome this, the paper proposes a convex quadratic programming-based inverse kinematics framework that outputs joint velocities while jointly optimizing task tracking accuracy, joint limit admissibility, and redundancy resolution. A novel controller-compatibility objective is introduced, incorporating actuator torque capacity weighting and command continuity. Feasibility with respect to joint limits is enforced via control-barrier-function-inspired constraints at the reference level, and slack variables are employed to handle task equation violations gracefully. Experimental validation on a seven-degree-of-freedom upper-limb exoskeleton demonstrates significant reductions in joint-limit impacts, bounded and admissible velocity outputs, and improved task tracking performance—all without requiring modifications to the downstream torque controller.

Abstract

This paper proposes actuator-aware inverse kinematics for torque-controlled redundant robots under joint-limit constraints. In the considered architecture, the inverse-kinematic output is not merely a purely kinematic joint-velocity command; it is the required joint velocity supplied to a downstream torque-level controller. Therefore, a small commanded task residual may not necessarily improve realized motion. The proposed method formulates a convex quadratic programming problem whose decision variable is the joint-level required velocity. Control barrier function style bounds impose reference-level joint-limit admissibility, while the task equation is handled through a penalized slack variable. Redundancy is resolved using a controller-compatibility objective that accounts for previous-command consistency and actuator torque-capacity weighting. The method is independent of the particular torque-level controller and can serve as an intermediate IK layer between an endpoint trajectory and a redundant robot controller. Experiments on a virtual-decomposition-controlled seven-degree-of-freedom upper-limb exoskeleton compare the method with standard inverse-kinematic baselines and a constrained task-preserving quadratic programming baseline. The results indicate lower limit-pushing commands, bounded admissible required velocities, and improved realized task behavior in the tested trajectory, without modifying the downstream controller.

Problem

• inverse kinematics
• torque-controlled robots
• joint-limit constraints
• redundant robots
• actuator-aware

Innovation

• actuator-aware inverse kinematics
• torque-controlled robots
• joint-limit admissibility
• redundancy resolution
• convex quadratic programming