5 papers
Task Inference Beyond Least Squares in Behavioral Foundation Models
Kuan-Hsun Tu, Chien-Sheng Chiang, Hsin-Wei Chen +2
Behavioral Foundation Models (BFMs) aim to solve a wide range of downstream tasks without test-time policy learning by inferring a task vector from the reward function. While effic…
Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching
Kuan-Hsun Tu, Hsuan-Chi Liu, Jia-Wei Liao +3
We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas standard conditional flow match…
COT-FM: Cluster-wise Optimal Transport Flow Matching
Chiensheng Chiang, Kuan-Hsun Tu, Jia-Wei Liao +2
We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved tra…
DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos
Jhen Hsieh, Kuan-Hsun Tu, Kuo-Han Hung +1
We present DexMan, an automated framework that converts human visual demonstrations into bimanual dexterous manipulation skills for humanoid robots in simulation. Operating directl…
RobotArena : Scalable Robot Benchmarking via Real-to-Sim Translation
Yash Jangir, Yidi Zhang, Pang-Chi Lo +7
The pursuit of robot generalists, agents capable of performing diverse tasks across diverse environments, demands rigorous and scalable evaluation. Yet real-world testing of robot…