3 accepted papers
This study addresses the challenge of multi-robot cooperative exploration under limited local observations in environments with dynamic robot populations. To achieve efficient area coverage within constrained timeframes, we propose the Multi-Robot Informative Planner (MIP), a novel reinforcement lea
"Continual learning aims at enabling a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which, however, limits their practical values due to privacy and memory c…
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowledge and addr…