GeoDEx: A Unified Geometric Framework for Tactile Dexterous and Extrinsic Manipulation under Force Uncertainty (2505.00647v1)

Abstract: Sense of touch that allows robots to detect contact and measure interaction forces enables them to perform challenging tasks such as grasping fragile objects or using tools. Tactile sensors in theory can equip the robots with such capabilities. However, accuracy of the measured forces is not on a par with those of the force sensors due to the potential calibration challenges and noise. This has limited the values these sensors can offer in manipulation applications that require force control. In this paper, we introduce GeoDEx, a unified estimation, planning, and control framework using geometric primitives such as plane, cone and ellipsoid, which enables dexterous as well as extrinsic manipulation in the presence of uncertain force readings. Through various experimental results, we show that while relying on direct inaccurate and noisy force readings from tactile sensors results in unstable or failed manipulation, our method enables successful grasping and extrinsic manipulation of different objects. Additionally, compared to directly running optimization using SOCP (Second Order Cone Programming), planning and force estimation using our framework achieves a 14x speed-up.

• The paper introduces GeoDEx, a geometric framework utilizing planes, cones, and ellipsoids to address force uncertainty in tactile dexterous and extrinsic robotic manipulation.
• GeoDEx demonstrates significant computational efficiency, achieving a 14x speed-up compared to Second Order Cone Programming (SOCP) methods.
• The framework enables robots to successfully perform stable grasping and extrinsic manipulation tasks despite the inherent noise and errors in tactile sensor data.

GeoDEx: A Unified Geometric Framework for Tactile Dexterous and Extrinsic Manipulation under Force Uncertainty

The paper introduces GeoDEx, a novel framework designed to advance the capabilities of dexterous robotic manipulation using tactile sensors. The framework addresses the limitations posed by the inaccuracies in force readings from tactile sensors and proposes a method to perform dexterous as well as extrinsic manipulation reliably despite these inaccuracies.

Framework Overview

GeoDEx tackles the problem of force uncertainty in robotic manipulation by leveraging a geometric approach. The framework incorporates estimation, planning, and control phases, utilizing geometric primitives—planes, cones, and ellipsoids—to represent force constraints and achieve robust manipulation. This geometric abstraction aids in handling the noise and errors typically associated with tactile sensor data.

Key Findings and Numerical Results

The empirical validation of GeoDEx demonstrates that direct reliance on noisy tactile sensor readings often results in unstable manipulations or outright failures. In contrast, the proposed framework facilitates successful execution of dexterous grasping and extrinsic manipulation tasks. An important performance metric presented is the computational efficiency: compared to Second Order Cone Programming (SOCP), the framework achieves a 14x speed-up. This significant improvement is crucial for real-time applications where quick processing is necessary.

Theoretical and Practical Implications

From a theoretical standpoint, GeoDEx provides a structured methodology for integrating tactile feedback into the control loops of robotic systems, which has traditionally been a challenging area due to sensor noise and calibration issues. This framework enables the use of tactile sensors not only for detecting the presence or absence of contact but also for nuanced force control, bringing tactile sensing closer to its full potential in robotic manipulation tasks.

Practically, the enhanced performance of GeoDEx in terms of stability and computational efficiency means that robots can perform more delicate operations, such as handling fragile objects or executing precise tasks like tool usage, with greater reliability. The methodology of employing geometric projections to mitigate sensor noise is a valuable advancement for real-world robotic applications where robustness and precision are paramount.

Speculation on Future Developments

Looking towards the future, the trajectory of AI research could see GeoDEx-type frameworks expanding their domain to include more sophisticated sensor fusion techniques, merging tactile data with visual and auditory inputs for more comprehensive environmental interaction. Additionally, advancements in sensor technology could complement this framework by providing higher fidelity force measurements, thus reducing the reliance on estimation and further improving manipulation outcomes.

In conclusion, GeoDEx presents a significant step in the integration of tactile sensing into robotic manipulation, addressing a critical problem with an innovative geometric approach. This framework not only improves current capabilities but also sets the stage for future advancements in AI and robotic dexterity.