必要なロボットは何か？グラフニューラルネットワークによる形態と制御の高速共適応
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks
深層NNとグラフNNを組み合わせ、限られた製造サイクル内でロボットの形態と制御をデータ効率的に共適応させる手法を提案し、シミュレーションで有効性を示した。
著者: Kevin Sebastian Luck, Roberto Calandra, Michael Mistry
分類: cs.RO, cs.LG, cs.NE
原文アブストラクト
The co-adaptation of robot morphology and behaviour becomes increasingly important with the advent of fast 3D-manufacturing methods and efficient deep reinforcement learning algorithms. A major challenge for the application of co-adaptation methods to the real world is the simulation-to-reality-gap due to model and simulation inaccuracies. However, prior work focuses primarily on the study of evolutionary adaptation of morphologies exploiting analytical models and (differentiable) simulators with large population sizes, neglecting the existence of the simulation-to-reality-gap and the cost of manufacturing cycles in the real world. This paper presents a new approach combining classic high-frequency deep neural networks with computational expensive Graph Neural Networks for the data-efficient co-adaptation of agents with varying numbers of degrees-of-freedom. Evaluations in simulation show that the new method can co-adapt agents within such a limited number of production cycles by efficiently combining design optimization with offline reinforcement learning, that it allows for the direct application to real-world co-adaptation tasks in future work
