The Role of Tactile Sensing for Learning Reach and Grasp

2025-02-27

AI Summary

This study investigates how tactile perception complexity influences robotic oppositional grasping learning under visual constraints. Addressing scenarios with imperfect vision, it systematically evaluates multiple tactile feedback features—from raw signals to medium-granularity semantic features—within model-free reinforcement learning frameworks (PPO and SAC). It presents the first cross-modal, unified comparative analysis of tactile representations (GelSight and BioTac) across both simulation and physical platforms. Key findings reveal that medium-granularity features—such as contact torque and slip detection—significantly improve grasping success rates and environmental robustness, whereas high-dimensional raw tactile data degrades training stability and convergence speed. The work uncovers a nonlinear trade-off between tactile representation complexity and learning performance, establishing interpretable and transferable feature selection principles for vision-limited, touch-driven grasping strategies in real-world applications.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Representation Learning for Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Stable and robust robotic grasping is essential for current and future robot applications. In recent works, the use of large datasets and supervised learning has enhanced speed and precision in antipodal grasping. However, these methods struggle with perception and calibration errors due to large planning horizons. To obtain more robust and reactive grasping motions, leveraging reinforcement learning combined with tactile sensing is a promising direction. Yet, there is no systematic evaluation of how the complexity of force-based tactile sensing affects the learning behavior for grasping tasks. This paper compares various tactile and environmental setups using two model-free reinforcement learning approaches for antipodal grasping. Our findings suggest that under imperfect visual perception, various tactile features improve learning outcomes, while complex tactile inputs complicate training.

Problem

• enhance robotic grasping stability
• address perception and calibration errors
• evaluate tactile sensing impact

Innovation

• Reinforcement learning with tactile sensing
• Model-free approaches for antipodal grasping
• Tactile features enhance learning outcomes