Large pretrained (eg," foundation") models exhibit distinct capabilities depending on the
domain of data they are trained on. While these domains are generic, they may only barely …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

How can we imbue robots with the ability to manipulate objects precisely but also to reason
about them in terms of abstract concepts? Recent works in manipulation have shown that …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Learning object-centric representations of complex scenes is a promising step towards
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …

Robotic manipulation can be formulated as inducing a sequence of spatial displacements:
where the space being moved can encompass an object, part of an object, or end effector. In …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

We present a new framework for semantic segmentation without annotations via clustering.
Off-the-shelf clustering methods are limited to curated, single-label, and object-centric …

Despite decades of research, general purpose in-hand manipulation remains one of the
unsolved challenges of robotics. One of the contributing factors that limit current robotic …

We approach the problem of learning by watching humans in the wild. While traditional
approaches in Imitation and Reinforcement Learning are promising for learning in the real …