Learning a universal policy across different robot morphologies can significantly improve
learning efficiency and enable zero-shot generalization to unseen morphologies. However …

In this paper, we investigate an offline reinforcement learning (RL) problem where datasets
are collected from two domains. In this scenario, having datasets with domain labels …

In this paper, we introduce a novel convex formulation that seamlessly integrates the
Material Point Method (MPM) with articulated rigid body dynamics in frictional contact …

Foundation models have indeed made a profound impact on various fields, emerging as
pivotal components that significantly shape the capabilities of intelligent systems. In the …

Precise manipulation that is generalizable across scenes and objects remains a persistent
challenge in robotics. Current approaches for this task heavily depend on having a …

Autonomous surgical robots have the potential to transform surgery and increase access to
quality health care. Advances in artificial intelligence have produced robots mimicking …

Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial
and error, to solve complex decision-making problems. Learning with little supervision …

A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is
more severe in mobile manipulation, where collecting demonstrations is harder than in …

Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by
the growth of required resources, expansive datasets and corresponding investments into …

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased
datasets. While curated datasets can help, challenges remain in generalizability and real …