Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit
constraints followed by expert agents from their demonstration data. As an emerging …

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 …

Vision Language Models (VLMs) have recently been adopted in robotics for their capability
in common sense reasoning and generalizability. Existing work has applied VLMs to …

Effective collaboration of dual-arm robots and their tool use capabilities are increasingly
important areas in the advancement of robotics. These skills play a significant role in …

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual
demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home …

Imitation learning from human demonstrations is an effective means to teach robots
manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more …

Increasingly large imitation learning datasets are being collected with the goal of training
foundation models for robotics. However, despite the fact that data selection has been of …

Simulation has enabled unprecedented compute-scalable approaches to robot learning.
However, many existing simulation frameworks typically support a narrow range of …

Robotic simulation today remains challenging to scale up due to the human efforts required
to create diverse simulation tasks and scenes. Simulation-trained policies also face …

Realizing scaling laws in embodied AI has become a focus. However, previous work has
been scattered across diverse simulation platforms, with assets and models lacking unified …