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Found 3 papers, 1 papers with code
Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network
We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints.
Learning from demonstration using products of experts: applications to manipulation and task prioritization
In particular, we show that the proposed approach can be extended to PoE with a nullspace structure (PoENS), where the model is able to recover tasks that are masked by the resolution of higher-level objectives.
Interaction-limited Inverse Reinforcement Learning
This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training.
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