Learning from demonstrations has shown to be an effective approach to robotic
manipulation, especially with the recently collected large-scale robot data with teleoperation …

Humans excel at dexterous manipulation; however, achieving human-level dexterity
remains a significant challenge for robots. Technological breakthroughs in the design of …

Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …

We study the problem of teaching humanoid robots manipulation skills by imitating from
single video demonstrations. We introduce OKAMI, a method that generates a manipulation …

Recent progress in imitation learning from human demonstrations has shown promising
results in teaching robots manipulation skills. To further scale up training datasets, recent …

Data scaling has revolutionized fields like natural language processing and computer vision,
providing models with remarkable generalization capabilities. In this paper, we investigate …

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

Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-
manipulation, and tabletop manipulation, each demanding a different mode of control. For …

Autoregressive sequence models, such as Transformer-based vision-language action (VLA)
policies, can be tremendously effective for capturing complex and generalizable robotic …

Humanoid robots require both robust lower-body locomotion and precise upper-body
manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body …