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arXiv · 2103.16092

Inferring the Geometric Nullspace of Robot Skills from Human Demonstrations

Caixia Cai·Ying Siu Liang·Nikhil Somani·Wu Yan
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Abstract

In this paper we present a framework to learn skills from human demonstrations in the form of geometric nullspaces, which can be executed using a robot. We collect data of human demonstrations, fit geometric nullspaces to them, and also infer their corresponding geometric constraint models. These geometric constraints provide a powerful mathematical model as well as an intuitive representation of the skill in terms of the involved objects. To execute the skill using a robot, we combine this geometric skill description with the robot's kinematics and other environmental constraints, from which poses can be sampled for the robot's execution. The result of our framework is a system that takes the human demonstrations as input, learns the underlying skill model, and executes the learnt skill with different robots in different dynamic environments. We evaluate our approach on a simulated industrial robot, and execute the final task on the iCub humanoid robot.

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Caixia Cai, Ying Siu Liang, Nikhil Somani, Wu Yan. 2021-03-30. Inferring the Geometric Nullspace of Robot Skills from Human Demonstrations. https://doi.org/10.1109/icra40945.2020.9197174

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