8 papers · 1 filter
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…
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…
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…