7 papers · 1 filter
ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
Sirui Chen, Chen Wang, Kaden Nguyen +2
Recent progress in imitation learning from human demonstrations has shown promising results in teaching robots manipulation skills. To further scale up training datasets, recent wo…
ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation
Wenlong Huang, Chen Wang, Yunzhu Li +2
Representing robotic manipulation tasks as constraints that associate the robot and the environment is a promising way to encode desired robot behaviors. However, it remains unclea…
TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
Yunfan Jiang, Chen Wang, Ruohan Zhang +2
Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulat…
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
Chengshu Li, Ruohan Zhang, Josiah Wong +32
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive s…
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation
Chen Wang, Haochen Shi, Weizhuo Wang +3
Imitation learning from human hand motion data presents a promising avenue for imbuing robots with human-like dexterity in real-world manipulation tasks. Despite this potential, su…
NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
Ruohan Zhang, Sharon Lee, Minjune Hwang +11
We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday a…