arXiv:2608. 13600v1 Announce Type: cross Abstract: We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones.
By Nicola Pitzalis, Eleni Nisioti, Antonio Carta, Davide Bacciu, Andrea Cossu
arXiv:2606. 00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions change.
By Purab Seth, Neil Shah, Kunal Jha, Samuel J. Gershman, Max Kleiman-Weiner, Wilka Carvalho
arXiv:2606. 03598v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation.
By Ziyang Chen, Shaoguang Wang, Weiyu Guo, Qianyi Cai, He Zhang, Pengteng Li, Yiren Zhao, Yandong Guo
arXiv:2606. 26183v1 Announce Type: cross Abstract: Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge.
By Zhihao Gu, Lin Wang
arXiv:2606. 19752v1 Announce Type: cross Abstract: Long-horizon robot manipulation policies trained with reward shaping can still exploit dense rewards through inefficient interaction, while rare efficient behaviors may be forgotten during training.
By Yinsen Jia, Boyuan Chen
arXiv:2606. 17889v1 Announce Type: cross Abstract: Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tasks share structure and risk interference.
By Kathrin Korte, Christian Medeiros Adriano, Joachim Winther Pedersen, Eleni Nisioti, Sebastian Risi