3 accepted papers
Movement Primitive (MP) is a promising Learning from Demonstration (LfD) framework, which is commonly used to learn movements from human demonstrations and adapt the learned movements to new task scenes. A major goal of MP research is to improve the adaptability of MP to various target positions and
Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem b
Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning a set of skills in a dynamic environment. However, it typically requires millions of the environmental interactions explored by agents, which is