Learning to Sequence and Blend Robot Skills via Differentiable Optimization
| Autor: | Noemie Jaquier, You Zhou, Julia Starke, Tamim Asfour |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Human-Computer Interaction
FOS: Computer and information sciences Computer Science - Robotics Control and Optimization Artificial Intelligence Control and Systems Engineering Mechanical Engineering Biomedical Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) Computer Science Applications |
| Popis: | In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduces a novel skill-agnostic framework that learns to sequence and blend skills based on differentiable optimization. Our approach encodes sequences of previously-defined skills as quadratic programs (QP), whose parameters determine the relative importance of skills along the task. Seamless skill sequences are then learned from demonstrations by exploiting differentiable optimization layers and a tailored loss formulated from the QP optimality conditions. Via the use of differentiable optimization, our work offers novel perspectives on multitask control. We validate our approach in a pick-and-place scenario with planar robots, a pouring experiment with a real humanoid robot, and a bimanual sweeping task with a human model. Accepted for publication in IEEE Robotics and Automation Letters. Video: https://youtu.be/00NXvTpL-YU, code: https://github.com/NoemieJaquier/sequencing-blending/ |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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