Humans use different modalities, such as speech, text, images, videos, etc., to communicate
their intent and goals with teammates. For robots to become better assistants, we aim to …

Generalization capabilities, or rather a lack thereof, is one of the most important unsolved
problems in the field of robot learning, and while several large scale efforts have set out to …

We introduce GROOT, an imitation learning method for learning robust policies with object-
centric and 3D priors. GROOT builds policies that generalize beyond their initial training …

To endow models with greater understanding of physics and motion, it is useful to enable
them to perceive how solid surfaces move and deform in real scenes. This can be formalized …

Robot models, particularly those trained with large amounts of data, have recently shown a
plethora of real-world manipulation and navigation capabilities. Several independent efforts …

Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …

We introduce LOTUS, a continual imitation learning algorithm that empowers a physical
robot to continuously and efficiently learn to solve new manipulation tasks throughout its …

Learning from Demonstrations, the field that proposes to learn robot behavior models from
data, is gaining popularity with the emergence of deep generative models. Although the …

Autoregressive sequence models, such as Transformer-based vision-language action (VLA)
policies, can be tremendously effective for capturing complex and generalizable robotic …

Imitation learning has proven to be a powerful tool for training complex visuo-motor policies.
However, current methods often require hundreds to thousands of expert demonstrations to …