Generating Robot Hands from Human Demonstrations

• URL: http://arxiv.org/abs/2606.20549v1
• Date: Thu, 18 Jun 2026 17:57:21 GMT
• Title: Generating Robot Hands from Human Demonstrations
• Abstract summary: We present a data-driven framework for generating robot hands from human demonstrations.<n>We generate robot hand designs using the same simple control policy used after fabrication.<n>We fabricated the mechanisms directly as one-piece articulated structures with print-in-place joints.
• Score: 14.581437959196398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robot learning has advanced rapidly in learning control, but learning the physical body of a robot remains much more difficult because jointly searching over design and control creates a very large combinatorial problem. Here, we present a data-driven framework for generating robot hands from human demonstrations. Instead of learning a complex controller together with each candidate design, we generate robot hand designs using the same simple control policy used after fabrication: matching fingertip positions through inverse kinematics. Using more than 4 million frames of human fingertip motion from everyday manipulation, our algorithm optimizes tree-structured robot hands to reproduce desired target motions. The framework produced both a 6-degree-of-freedom (DoF) general-purpose hand and lower-DoF task-specific hands with spatial four-bar mimic joints. To accelerate the search over designs, we trained a reinforcement-learning (RL) actor to propose good hand designs and joint angles, reducing search time from hours to minutes. We fabricated the mechanisms directly as one-piece articulated structures with print-in-place joints. In real-world experiments, the 6-DoF hand achieved highly accurate teleoperated fingertip tracking better than available commercial robot hands, whereas the specialized 3-DoF hands reproduced structured human and synthetic trajectories with reduced mechanical complexity. These results showed that large-scale human motion data can be used not only to train robot controllers but also as a reference for optimizing and generating the physical embodiment of robots.

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