October 3, 2025Open Access

Tac2Motion: Contact-Aware Reinforcement Learning with Tactile Feedback for Robotic Hand Manipulation

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Proposed framework enhances robotic hand manipulation through tactile feedback and reinforcement learning, suggesting improved grasping techniques.

The proposed Tac2Motion framework improves manipulation tasks using tactile feedback, which enhances performance.
Tactile sensing-based reward shaping leads to higher data efficiency and robust control compared to traditional methods.
Verification on opening a lid scenario indicates strong generalization across object types and dynamic conditions.
The control policy's demonstrated transferability to the real world with Shadow Robot highlights practical applicability.

Kim et al. (2025) studied this question.

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Proposed framework enhances robotic hand manipulation through tactile feedback and reinforcement learning, suggesting improved grasping techniques.