Large vision-language models have achieved outstanding performance, but their size and
computational requirements make their deployment on resource-constrained devices and …

Perceiving and manipulating 3D articulated objects in diverse environments is essential for
home-assistant robots. Recent studies have shown that point-level affordance provides …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local
trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In …

We present RoboGen, a generative robotic agent that automatically learns diverse robotic
skills at scale via generative simulation. RoboGen leverages the latest advancements in …

Imitation learning from a large set of human demonstrations has proved to be an effective
paradigm for building capable robot agents. However, the demonstrations can be extremely …

We present Orbit, a unified and modular framework for robot learning powered by Nvidia
Isaac Sim. It offers a modular design to easily and efficiently create robotic environments …

Abstract Visual Reinforcement Learning (Visual RL), coupled with high-dimensional
observations, has consistently confronted the long-standing challenge of out-of-distribution …

Learning a generalizable object manipulation policy is vital for an embodied agent to work in
complex real-world scenes. Parts, as the shared components in different object categories …

To enable general-purpose robots, we will require the robot to operate daily articulated
objects as humans do. Current robot manipulation has heavily relied on using a parallel …