Language provides a way to break down complex concepts into digestible pieces. Recent
works in robot imitation learning use language-conditioned policies that predict actions given …

The creation of large, diverse, high-quality robot manipulation datasets is an important
stepping stone on the path toward more capable and robust robotic manipulation policies. …

Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert
demonstrations. However, policies learned through IL suffer from state distribution shift at test time, …

In supervised learning, the question of data quality and curation has been sidelined in recent
years in favor of increasingly more powerful and expressive models that can ingest internet-…

Robot-assisted feeding in household environments is challenging because it requires robots
to generate trajectories that effectively bring food items of varying shapes and sizes into the …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision, …

Diffusion models are powerful generative models but suffer from slow sampling, often taking
1000 sequential denoising steps for one sample. As a result, considerable efforts have been …

Robot chain-of-thought reasoning (CoT) -- wherein a model predicts helpful intermediate
representations before choosing actions -- provides an effective method for improving the …

Transporting suspended payloads is challenging for autonomous aerial vehicles because
the payload can cause significant and unpredictable changes to the robot's dynamics. These …

Deep reinforcement learning provides a promising approach for vision-based control of real-world
robots. However, the generalization of such models depends critically on the quantity …