3 accepted papers
Unsupervised skill discovery acquires a diverse repertoire of skills through intrinsic motivation, offering the potential to alleviate the labor-intensive reward engineering in reinforcement learning and the reliance on costly task-specific data in imitation learning. However, such methods typically…
Recent advances in retrieval-based in-context learning (ICL) train the retriever using a classification objective, which categorizes in-context examples (ICEs) into the most useful and the rest based on absolute scores. However, during inference, ICEs are retrieved by score ranking rather than class…
Analyzing and forecasting trajectories of agents like pedestrians plays a pivotal role for embodied intelligent applications. The inherent indeterminacy of human behavior and complex social interaction among a rich variety of agents makes this task more challenging than common time-series forecastin