Tactile Dexterity: Manipulation Primitives with Tactile Feedback (2002.03236v2)

Abstract: This paper develops closed-loop tactile controllers for dexterous robotic manipulation with a dual-palm robotic system. Tactile dexterity is an approach to dexterous manipulation that plans for robot/object interactions that render interpretable tactile information for control. We divide the role of tactile control into two goals: 1) control the contact state between the end-effector and the object (contact/no-contact, stick/slip) by regulating the stability of planned contact configurations and monitoring undesired slip events; and 2) control the object state by tactile-based tracking and iterative replanning of the object and robot trajectories. Key to this formulation is the decomposition of manipulation plans into sequences of manipulation primitives with simple mechanics and efficient planners. We consider the scenario of manipulating an object from an initial pose to a target pose on a flat surface while correcting for external perturbations and uncertainty in the initial pose of the object. We experimentally validate the approach with an ABB YuMi dual-arm robot and demonstrate the ability of the tactile controller to react to external perturbations.

• The paper introduces a tactile-based framework that decomposes manipulation tasks into primitives like grasp, push, pivot, and pull.
• The paper validates its approach using the ABB YuMi dual-arm robot, achieving robust tracking and real-time trajectory replanning under controlled conditions.
• The paper highlights theoretical implications for integrating tactile feedback with traditional vision methods to improve autonomous, dexterous robotic manipulation.

Tactile Dexterity in Robotic Manipulation: An Analytical View

The paper "Tactile Dexterity: Manipulation Primitives with Tactile Feedback" provides an in-depth exploration of how tactile sensing can enhance dexterous robotic manipulation. It posits a tactile-based approach leveraging closed-loop controllers in a dual-palm robotic system, which augments traditional vision-based methods that fall short in tasks requiring precise contact interactions. The paper distinguishes itself with two primary objectives: managing the contact state between the robot end-effector and the object, and controlling the object’s state via tactile-based tracking and replanning.

Core Proposition and Framework

The framework for tactile dexterity introduced in the paper revolves around the decomposition of manipulation plans into a sequence of predefined manipulation primitives—each governed by simple mechanical models and geared with efficient planners. These primitives include grasp, push, pivot, and pull interactions. Such decomposition allows for leveraging tactile feedback to refine control over object interactions, a feature prominently validated using the ABB YuMi dual-arm robot. The tactile dexterity approach is hindered by certain assumptions: known geometry and frictional properties, rigid-body interaction models, and a quasi-static interaction hypothesis. Despite these simplifications, the approach exemplifies significant potential in handling complex manipulation tasks with minimal uncertainty.

Numerical Results and Experimental Validation

The authors present strong numerical results, particularly the system's resilience to external perturbations and its ability to adapt to unexpected changes in object pose by real-time trajectory replanning. The experiments illustrate a noteworthy capability in tracking and regulating the pose of an object, effectively exploiting tactile feedback to maximize contact stability and mitigate slippage—a vital factor in maintaining the integrity of dexterous tasks. While the manipulation system operates under controlled environments—flat surfaces, known object properties—it demonstrates a strong foundation for developing more adaptive and perceptive robotic systems.

Theoretical and Practical Implications

On a theoretical plane, this paper charts a course towards a more integrated understanding of dexterous manipulation, suggesting tactile feedback can substantially augment the control strategies employed in robotic applications. By emphasizing locally optimal manipulative actions through high-resolution tactile sensing, the paper encourages future work to explore and expand on these foundational primitives. Practically, the tactile control method could have implications for a wide range of industries—from manufacturing automation where object manipulation by robots needs to be precise, to healthcare robotics where tactile sensitivity is crucial.

Speculation on Future Developments

As for artificial intelligence, tactility could bridge substantial gaps between machine perception and autonomous decision-making. Further research may delve into enhancing tactile data integration with visual inputs, broadening the field of actuator intelligence. Expanding tactile dexterity to include dynamic environments, multi-object contexts, and complex manipulative sequences presents an exciting frontier. Future endeavors might also aim at reducing reliance on predefined primitives by employing machine learning techniques to autonomously discover manipulation strategies that leverage tactile feedback.

In conclusion, "Tactile Dexterity: Manipulation Primitives with Tactile Feedback" presents a significant stride towards a comprehensive, tactile-centric approach in robotic manipulation. By highlighting manipulation sequences that render interpretable tactile feedback, the authors lay down groundwork that future research can build upon to create more autonomous, adaptable, and intelligent robotic systems.