Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive
knowledge and exhibit promising reasoning abilities; however, they still struggle to perform …

In recent years, the Robotics field has initiated several efforts toward building generalist
robot policies through large-scale multi-task Behavior Cloning. However, direct deployments …

Recent advances in behavior cloning (BC), like action-chunking and diffusion, have led to
impressive progress. Still, imitation alone remains insufficient for tasks requiring reliable and …

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled
datasets, exhibit powerful capabilities in understanding complex patterns and generating …

Generative Flow Networks (GFlowNets), a class of generative models have recently
emerged as a suitable framework for generating diverse and high-quality molecular …

The modern paradigm in machine learning involves pre-training on diverse data, followed
by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning via …

Decision Transformers have recently emerged as a new and compelling paradigm for offline
Reinforcement Learning (RL), completing a trajectory in an autoregressive way. While …

Recent advances in behavior cloning (BC), like action-chunking and diffusion, have led to
impressive progress. Still, imitation alone remains insufficient for tasks requiring reliable and …

Humans possess the ability to draw on past experiences explicitly when learning new tasks
and applying them accordingly. We believe this capacity for self-referencing is especially …

Describing skills in natural language has the potential to provide an accessible way to inject
human knowledge about decision-making into an AI system. We present MaestroMotif, a …