TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments
This work addresses the challenge of vision-free robotic object manipulation in confined spaces, providing a practical solution for scenarios where vision is unavailable.
TACTFUL enables a multi-fingered robot to autonomously explore confined workspaces using only tactile sensing, achieving 77% success and 0.015 m average reconstruction error for object localization and identification, outperforming baselines.
Humans effortlessly locate and identify objects by touch alone, even without vision. In contrast, robotic systems rely heavily on vision and struggle with autonomous tactile exploration and object identification. We present TACTFUL, a vision-free tactile exploration framework that enables a multi-fingered robot to autonomously explore confined workspaces, discover objects through contact, and identify them via tactile reconstruction. Trained entirely on real hardware without simulation, our system learns a single policy that balances global workspace exploration with local surface refinement through a dynamic reward schedule. Our results demonstrate that tactile sensing, when paired with structured learning, can serve as an effective primary modality for object-level reasoning, achieving 77% success with 0.015 m average reconstruction error and outperforming baseline approaches on real-world objects.