TacMan-Turbo: Proactive Tactile Control for Robust and Efficient Articulated Object Manipulation

Adept manipulation of articulated objects is essential for robots to operate successfully in human environments. Such manipulation requires both effectiveness—reliable operation despite uncertain object structures—and efficiency—swift execution with minimal redundant steps and smooth trajectories. Existing approaches struggle to achieve both objectives simultaneously: methods relying on predefined kinematic models lack robustness when encountering structural variations, while the tactile-informed approach achieves robust manipulation but sacrifices efficiency through reactive, step-by-step execute-and-recover cycles. To address this challenge, this paper introduces TacMan-Turbo, a proactive tactile control framework that unifies the cycles into a continuous control loop. Our key insight is to interpret tactile signals temporally in addition to spatially: instead of treating contact deviations merely as instantaneous error signals requiring immediate compensation, we analyze sequential tactile observations to reveal local kinematic information. This temporal perspective enables our controller to predict future object states and proactively modulate manipulation velocities, eliminating the previous execute-and-recover cycle. In evaluations across 200 diverse simulated articulated objects and real-world experiments, our approach maintains a near-perfect performance while significantly enhancing time efficiency, action efficiency, and trajectory smoothness (all p-values < 0.0001). These results demonstrate that tactile feedback serves a dual purpose: providing not only spatial error signals for reactive control but also critical temporal structural information that enables proactive prediction and control. By enabling robots to manipulate articulated objects both reliably and efficiently, this work advances robot capabilities for seamless operation in dynamic, human-centric environments.