We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable
robot manipulation. GR-2 is first pre-trained on a vast number of Internet videos to capture …

Scene representation has been a crucial design choice in robotic manipulation systems. An
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …

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 …

Imitation learning provides an efficient way to teach robots dexterous skills; however,
learning complex skills robustly and generalizablely usually consumes large amounts of …

Despite recent advances in improving the sample-efficiency of reinforcement learning (RL)
algorithms, designing an RL algorithm that can be practically deployed in real-world …

Humanoid robots capable of autonomous operation in diverse environments have long
been a goal for roboticists. However, autonomous manipulation by humanoid robots has …

Utilizing Vision-Language Models (VLMs) for robotic manipulation represents a novel
paradigm, aiming to enhance the model's ability to generalize to new objects and …

Scene representation is a crucial design choice in robotic manipulation systems. An ideal
representation is expected to be 3D, dynamic, and semantic to meet the demands of diverse …

Despite significant progress in robotics and embodied AI in recent years, deploying robots
for long-horizon tasks remains a great challenge. Majority of prior arts adhere to an open …

Task Parametrized Gaussian Mixture Models (TP-GMM) are a sample-efficient method for
learning object-centric robot manipulation tasks. However, there are several open …