Robotic Long-Horizon Manipulation with Bayesian Non-parametric Skill Priors

Excerpt

arXiv:2503.21975v2 Announce Type: replace-cross Abstract: Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. Existing skill-based approaches often assume a fixed parametric prior (e.g., a single Gaussian), limiting their ability to capture diverse and multi-modal skill structures required for complex tasks. We propose a Bayesian non-parametric skill prior that models temporally extended skills in a st