Newest papers

What Novice Operators See Changes What the Policy Learns: A User Study of Keypoint Hand Visual Representation for Teleoperated Demonstration

Amane Yamaguchi, Shohei Kamiguchi, Masashi Hamaya, Shigeo Yoshida, Takuji Narumi, Mai Nishimura · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27907 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Imitasi belajar poin semakin bergantung pada operator pemula, yang demonstrasi kurang dari para ahli dalam jumlah dan konsistensi antara uji coba. Operator bertindak pada apa yang mereka tunjukkan, namun bagaimana rendering robot mempengaruhi demonstrasi mereka, dan kebijakan yang dilatih pada mereka, belum diperiksa. Kami mempelajari pertanyaan ini dengan PointHand, antarmuka teleoperasi yang membuat pada tangan robot multi-jari yang sukses sebagai titik kunci yang terbatas pada ujung jari dan persendian, titik kunci yang sama tidak dapat diperhatikan. Dalam tugas simulasi mug-in-box, 18 pemula dan 4 ahli telah mengumpulkan uji coba 2.200 di bawah tiga rendering: bentuk nyata dari tangan, semua titik kunci ditekankan pada tangan robot multi-jari, atau jari dasar saja ditekankan. Para pemula yang menunjukkan jari dan jari indeks ditekankan tidak dapat mengumpulkan 441 demonstrasi

Machine translation (Meta M2M100); the English original follows.

Abstract

Imitation learning increasingly relies on novice operators, whose demonstrations fall short of experts' in number and inter-trial consistency. Operators act on what they are shown, yet how the rendering of the robot affects their demonstrations, and the policies trained on them, has not been examined. We study this question with PointHand, a teleoperation interface that renders a multi-fingered robot hand as sparse keypoints at its fingertips and joints, the same keypoints a policy can observe. In a simulated mug-in-box task, 18 novices and 4 experts collected 2,200 trials under three renderings: the real shape of the hand, all keypoints emphasized, or the thumb and index finger alone emphasized. The novices shown the thumb and index finger emphasized collected 441 successful demonstrations from 600 trials, against 320 for those shown the real shape. We then trained diffusion policies on these demonstrations, replacing the scene point cloud and proprioceptive state they observe with the hand keypoints and two object keypoints. Within 400 steps these demonstrations went from the weakest of the three novice conditions under the point-cloud policy (46.3%) to the strongest under the keypoint policy (57.3%), a gain of +11.0 percentage points in all five seeds. Those collected with all keypoints emphasized changed by at most +0.7 percentage points, and in no more than three seeds. The value of a demonstration therefore depends on the rendering shown during collection and on what the policy observes together. A shared representation can pay off on both sides, in the number of successful demonstrations and in the success rate of the policy trained on them. Project page: https://omron-sinicx.github.io/point-hand/

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Teleoperation, Computer science, Rendering (computer graphics), Artificial intelligence, Robot, Computer vision

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange