Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
This addresses the challenge of fast online adaptation for robots in real-world scenarios, though it is incremental as it builds on existing meta-learning and model-based reinforcement learning methods.
The paper tackles the problem of robots adapting to diverse, unforeseen situations like motor failures or rocky terrain by proposing FAMLE, which meta-trains multiple initial parameter sets and selects the most suitable one, enabling adaptation in significantly fewer time-steps compared to baselines.
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points. However, in the real world, a robot might encounter any situation starting from motor failures to finding itself in a rocky terrain where the dynamics of the robot can be significantly different from one another. In this paper, first, we show that when meta-training situations (the prior situations) have such diverse dynamics, using a single set of meta-trained parameters as a starting point still requires a large number of observations from the real system to learn a useful model of the dynamics. Second, we propose an algorithm called FAMLE that mitigates this limitation by meta-training several initial starting points (i.e., initial parameters) for training the model and allows the robot to select the most suitable starting point to adapt the model to the current situation with only a few gradient steps. We compare FAMLE to MBRL, MBRL with a meta-trained model with MAML, and model-free policy search algorithm PPO for various simulated and real robotic tasks, and show that FAMLE allows the robots to adapt to novel damages in significantly fewer time-steps than the baselines.