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Found 3 papers, 2 papers with code
Learning Contact Dynamics using Physically Structured Neural Networks
Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics.
Probabilistic Active Meta-Learning
However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner?
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e. g., robotics, animal experiments or drug design.
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