Diverse Policy Learning via Random Obstacle Deployment for Zero-Shot Adaptation

In this letter, we propose a novel reinforcement learning framework that enables zero-shot policy adaptation in environments with unseen, dynamically changing obstacles. Adopting the idea that learning a policy capable of generating diverse actions is key to achieving such adaptability, our primary contribution is a novel learning algorithm that incorporates random obstacle deployment, enabling the policy to explore and learn diverse actions. This method overcomes the limitations of existing diverse policy learning approaches, which primarily rely on mutual information maximization to increase diversity. To enable zero-shot dynamic adaptation, our method further involves two key components: a state-dependent latent skill sampler and a motion predictor. We sample multiple skill variables at each state using a skill sampler, then filter out unsafe skills using a motion predictor, and consequently execute actions corresponding to safe skills. Compared to existing methods, our experiments demonstrate that proposed method generates significantly more diverse actions and adapts better to dynamically changing environments, making it highly effective for tasks with varying constraints such as moving obstacles.