Adaptation of Quadruped Robot Locomotion with Meta-Learning
This addresses the challenge of robot adaptability in dynamic environments, though it is incremental as it builds on existing meta-learning methods.
The paper tackled the problem of enabling robots to adapt to various locomotion tasks without retraining from scratch, achieving performance comparable to single-task training.
Animals have remarkable abilities to adapt locomotion to different terrains and tasks. However, robots trained by means of reinforcement learning are typically able to solve only a single task and a transferred policy is usually inferior to that trained from scratch. In this work, we demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks. The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.