This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

While large-scale robotic systems typically rely on textual instructions for tasks, this work
explores a different approach: can robots infer the task directly from observing humans? This …

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 …

We explore how intermediate policy representations can facilitate generalization by
providing guidance on how to perform manipulation tasks. Existing representations such as …

Reinforcement Learning (RL) mathematically formulates decision-making with Markov
Decision Process (MDP). With MDPs, researchers have achieved remarkable breakthroughs …

This paper introduces MotionGlot, a model that can generate motion across multiple
embodiments with different action dimensions, such as quadruped robots and human …

Scaling laws have driven remarkable progress across machine learning domains like
language modeling and computer vision. However, the exploration of scaling laws in …

While learning from demonstrations is powerful for acquiring visuomotor policies, high-
performance imitation without large demonstration datasets remains challenging for tasks …

Deep learning models have achieved significant success across various applications but
continue to struggle with tasks requiring complex reasoning over sequences, such as …

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of
learning complex manipulation behaviors on an arm equipped with a three-fingered robotic …