3 accepted papers
A central challenge in image-based Model-Based Reinforcement Learning (MBRL) is to learn representations that distill essential information from irrelevant visual details. While promising, reconstruction-based methods often waste capacity on large task-irrelevant regions. Decoder-free methods instea…
Vision-based robot manipulation systems often suffer from performance degradation under domain shifts in visual inputs. While data augmentation is commonly employed in reinforcement learning, its application in imitation learning remains relatively underexplored. Our preliminary experiments indicate…
Tactile information holds promise for enhancing the manipulation capabilities of multi-fingered robots. In tasks such as in-hand manipulation, where robots frequently switch between contact and non-contact states, it is important to address the partial observability of tactile sensors and to properl…