  Few-shot adaptation is a challenging problem in the context of
simulation-to-real transfer in robotics, requiring safe and informative data
collection. In physical systems, additional challenge may be posed by domain
noise, which is present in virtually all real-world applications. In this
paper, we propose to perform few-shot adaptation of dynamics models in noisy
conditions using an uncertainty-aware Kalman filter-based neural network
architecture. We show that the proposed method, which explicitly addresses
domain noise, improves few-shot adaptation error over a blackbox adaptation
LSTM baseline, and over a model-free on-policy reinforcement learning approach,
which tries to learn an adaptable and informative policy at the same time. The
proposed method also allows for system analysis by analyzing hidden states of
the model during and after adaptation.
