Inferring the Geometric Nullspace of Robot Skills from Human Demonstrations

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 ge...

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Bibliographic Details
Main Authors: Cai, Caixia, Liang, Ying Siu, Somani, Nikhil, Yan, Wu
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Data models
Grasping
Manifolds
Service robots
Task analysis
Online Access:Request full text
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Description
Summary: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.
ISSN:2577-087X
DOI:10.1109/ICRA40945.2020.9197174