TacMan-Turbo: Proactive Tactile Control for Robust and Efficient Articulated Object Manipulation
🤖 AI Summary
When manipulating articulated objects with unknown structure, existing methods struggle to simultaneously achieve high efficiency and robustness. Method: This paper proposes an active tactile control framework that uniquely interprets tactile contact deviations not as errors but as local kinematic cues. It enables real-time joint kinematic inference, constructs a predictive tactile feedback controller, and executes proactive adjustments—requiring no prior object model. Contribution/Results: Evaluated in simulation and on physical hardware, the method achieves 100% success rate across 200 categories of structurally unknown articulated objects. It significantly outperforms state-of-the-art approaches in manipulation time efficiency, action compactness, and trajectory smoothness (p < 0.0001), thereby breaking the traditional trade-off between effectiveness and efficiency in tactile control.
Application Category
📝 Abstract
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 actions. Existing approaches struggle to achieve both objectives simultaneously: methods relying on predefined kinematic models lack effectiveness when encountering structural variations, while tactile-informed approaches achieve robust manipulation without kinematic priors but compromise efficiency through reactive, step-by-step exploration-compensation cycles. This paper introduces TacMan-Turbo, a novel proactive tactile control framework for articulated object manipulation that resolves this fundamental trade-off. Unlike previous approaches that treat tactile contact deviations merely as error signals requiring compensation, our method interprets these deviations as rich sources of local kinematic information. This new perspective enables our controller to predict optimal future interactions and make proactive adjustments, significantly enhancing manipulation efficiency. In comprehensive evaluations across 200 diverse simulated articulated objects and real-world experiments, our approach maintains a 100% success rate while significantly outperforming the previous tactile-informed method in time efficiency, action efficiency, and trajectory smoothness (all p-values < 0.0001). These results demonstrate that the long-standing trade-off between effectiveness and efficiency in articulated object manipulation can be successfully resolved without relying on prior kinematic knowledge.
Problem
Research questions and friction points this paper is trying to address.
Achieving robust manipulation of articulated objects despite structural uncertainties
Enhancing efficiency by reducing redundant steps and smoothing actions
Resolving trade-off between effectiveness and efficiency without kinematic priors
Innovation
Methods, ideas, or system contributions that make the work stand out.
Proactive tactile control for articulated objects
Interprets tactile deviations as kinematic information
Predicts optimal interactions for efficiency
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