Learning to Sequence and Blend Robot Skills via Differentiable Optimization
@article{Jaquier2022LearningTS, title={Learning to Sequence and Blend Robot Skills via Differentiable Optimization}, author={No{\'e}mie Jaquier and You Zhou and Julia Starke and Tamim Asfour}, journal={IEEE Robotics and Automation Letters}, year={2022}, volume={7}, pages={8431-8438} }
In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This letter 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…
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Hierarchical Policy Blending As Optimal Transport
- Computer ScienceArXiv
- 2022
This hierarchical framework adapts the weights of low-level reactive expert policies, adding a look-ahead planning layer on the parameter space of a product of expert policies and agents, paving the way for new applications of optimal transport to robot control.
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