Before revision:  We present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to ignore noisy distracting regions while searching for novelty in the learnable regions . It clusters the goal space and estimates ALP for each cluster. These ALP estimates can then be used to detect the distracting regions, and build a prior that enables further goal sampling mechanisms to ignore them .
After revision:  We present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to ignore noisy distracting regions while searching for novelty in the learnable regions to both improve overall performance and avoid catastrophic URLetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches .