9 citations · 18 across the 9 of their papers we have counts for
9 papers
Skills in Weights, Memory in Code: Hybrid Learning for Memory-Dependent Robot Manipulation
Yunhao Zhao, Zhenyang Ni, Haoyang Chen +2
Modern vision-language-action (VLA) policies have acquired broad manipulation skills, but typically generate each action chunk from the current observation or a short fixed-length…
SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
Nadun Ranawaka, Josiah Wong, Wei-Lin Pai +15
Training and evaluating robot policies in the real world is costly and difficult to scale. We introduce SimFoundry, a modular and automated system for zero-shot real-to-sim scene c…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Chain-of-Modality: Learning Manipulation Programs from Multimodal Human Videos with Vision-Language-Models
Chen Wang, Fei Xia, Wenhao Yu +6
Learning to perform manipulation tasks from human videos is a promising approach for teaching robots. However, many manipulation tasks require changing control parameters during ta…
Automated Creation of Digital Cousins for Robust Policy Learning
Tianyuan Dai, Josiah Wong, Yunfan Jiang +5
Training robot policies in the real world can be unsafe, costly, and difficult to scale. Simulation serves as an inexpensive and potentially limitless source of training data, but…
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
Manling Li, Shiyu Zhao, Qineng Wang +12
We aim to evaluate Large Language Models (LLMs) for embodied decision making. While a significant body of work has been leveraging LLMs for decision making in embodied environments…
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…
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…