加入力反馈让机器人抓取更精准，尤其擅长处理精细物体。
Just Add Force for Contact-Rich Robot Policies
- 用自研电流法采集力反馈数据，无需额外硬件
- 带力反馈的扩散模型抓取成功率显著提升，延迟降低近4倍
- 适合研究触觉感知与复杂操作的开发者
学习端到端机器人策略时，常规轨迹仅包含末端执行器位置、夹爪状态、工作区图像和语言信息，难以实现精细抓取。为此，我们收集并公开了130条成功抓取30种不同物体的力反馈轨迹。采用基于电流的力感测方法，虽有噪声但对夹爪无依赖且无需额外硬件。训练并评估两种扩散策略：一种使用（力反馈）的轨迹，另一种仅用（位置）轨迹。结果表明，带力反馈的策略在精细抓取任务中表现更优，能泛化至未见物体，并将抓取策略延迟降低近4倍，相比基于大语言模型的方法。在有限数据下取得良好效果，呼吁未来数据集更多纳入力觉等触觉信息，以支持更鲁棒的接触密集型机器人基础模型。数据、代码、模型与视频可访问 https://justaddforce.github.io/。
原文摘要 · Abstract (English)
Robot trajectories used for learning end-to-end robot policies typically contain end-effector and gripper position, workspace images, and language. Policies learned from such trajectories are unsuitable for delicate grasping, which require tightly coupled and precise gripper force and gripper position. We collect and make publically available 130 trajectories with force feedback of successful grasps on 30 unique objects. Our current-based method for sensing force, albeit noisy, is gripper-agnostic and requires no additional hardware. We train and evaluate two diffusion policies: one with (forceful) the collected force feedback and one without (position-only). We find that forceful policies are superior to position-only policies for delicate grasping and are able to generalize to unseen delicate objects, while reducing grasp policy latency by near 4x, relative to LLM-based methods. With our promising results on limited data, we hope to signal to others to consider investing in collecting force and other such tactile information in new datasets, enabling more robust, contact-rich manipulation in future robot foundation models. Our data, code, models, and videos are viewable at https://justaddforce.github.io/.
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