From the 1 of 6 linked papers with an AI index.
6 papers
Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
Woo Chul Shin, Zhenyang Chen, Alfred Cueva +5
The paper presents Static In, Dynamic Out (SIDO), a method that augments static-object demonstrations with counterfactual actions to enable visuomotor policies to handle moving obj…
EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
Yangcen Liu, Shuo Cheng, Xinchen Yin +6
Dexterous manipulation is limited by the cost of collecting large-scale robot demonstrations. Egocentric human videos offer a scalable source of diverse manipulation behaviors, but…
Compositional Visual Planning via Inference-Time Diffusion Scaling
Yixin Zhang, Yunhao Luo, Utkarsh Aashu Mishra +3
Diffusion models excel at short-horizon robot planning, yet scaling them to long-horizon tasks remains challenging due to computational constraints and limited training data. Exist…
ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation
Yangcen Liu, Woo Chul Shin, Yunhai Han +3
Learning robot manipulation from abundant human videos offers a scalable alternative to costly robot-specific data collection. However, domain gaps across visual, morphological, an…
SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung +8
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined…
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige +5
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent o…