无需视觉，机器人靠触觉在狭小空间自主找物识物
TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments
- 用触觉驱动探索，动态奖励机制平衡全局搜索与局部精调
- 真实场景下77%成功率，重建误差仅0.015米，优于基线方法
- 适合无视觉或光照受限环境下的机器人自主操作任务
人类即使在无视觉条件下也能依靠触觉轻松定位和识别物体。相比之下，机器人系统严重依赖视觉，在自主触觉探索与物体识别方面表现不佳。我们提出TACTFUL，一种完全不依赖视觉的触觉探索框架，使多指机器人能够在狭小工作空间中自主探索，通过接触发现物体，并利用触觉重建实现识别。系统在真实硬件上训练，无需仿真，学习单一策略，通过动态奖励调度实现全局空间探索与局部表面优化的平衡。实验表明，结合结构化学习的触觉感知可作为物体级推理的主要模态，在真实物体上实现77%的成功率，平均重建误差为0.015米，显著优于基线方法。
原文摘要 · Abstract (English)
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.
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