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/.

翻译：用于学习端到端机器人策略的轨迹通常包含末端执行器与夹爪位置、工作空间图像及语言信息。基于此类轨迹学习的策略难以适用于精细抓取任务，因为这类任务需要夹爪力与夹爪位置实现紧密耦合与精确控制。我们采集并公开了130条包含力反馈的成功抓取轨迹，涉及30种不同物体。我们采用的基于电流的力传感方法虽存在噪声，但具备夹爪无关性且无需额外硬件。我们训练并评估了两种扩散策略：一种使用采集的力反馈数据（强力策略），另一种不使用（仅位置策略）。研究发现，在精细抓取任务中，强力策略显著优于仅位置策略，能够泛化至未见过的精细物体，同时相较于基于LLM的方法，抓取策略延迟降低近4倍。基于有限数据取得的积极成果，我们期望推动学界在新数据集中重视力反馈及其他触觉信息的采集，为未来机器人基础模型实现更鲁棒、接触密集型的操作提供支持。我们的数据、代码、模型及演示视频可通过https://justaddforce.github.io/获取。