SwapBot：通过可互换末端执行器与触觉感知实现以工具为中心的机器人操作
SwapBot: Tool-Centric Robot Manipulation via Interchangeable End-Effectors and Tactile Sensing
Yunchu Zhang, Ray Song, Junhan Jiao, Liyiming Ke, Yuchen Yang, Ruihan Gao, Emma Romig, Siddhartha Srinivasa 等 9 位作者
AI 速览：SwapBot 提出一种模块化、以工具为中心的末端执行器系统，用低成本适配器把常见家用工具快速转换为机器人末端执行器，并结合触觉传感与模仿学习完成接触丰富的工具操作任务。
Robot manipulation in everyday environments inherently involves substantial tool usage. Seemingly simple household tasks, such as cooking pan-fried tofu, require forceful cutting, delicate brushing, and precise placement using different tools under contact-rich interaction. Thus, the ability to autonomously swap and use task-appropriate tools is important for tool-centric manipulation. We introduce SwapBot, a modular, tool-centric end effector system featuring low-cost adapters that quickly convert common household tools into robot end effectors. Rather than pursuing a single universal gripper, SwapBot adopts a unified design principle that combines a fixed robot-side coupling interface with low-cost, tool-side adapters, enabling compatibility with diverse tools, including articulated ones, and multiple robot platforms. Each adapter integrates compact tactile sensors, supports fast autonomous swapping, and interfaces with an intuitive teleoperation system for efficient demonstration collection. User studies show that, during teleoperation, SwapBot reduces task completion time and operator workload. Using collected demonstrations on the robot, we train imitation learning policies to successfully perform tool-centric tasks like cooking tofu, stirring salad, flipping meat. Our ablation study shows that using tactile-augmented demonstrations improves policy performance across contact-rich skills, increasing success rates by 15 to 25 percentage points over RGB-only policies. These results highlight modular, tactile-equipped tools as a practical and extensible pathway toward robust and capable robot manipulation. Project website https://personalrobotics.github.io/SwapBot.