4 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion
Chih-Hai Su, Tsung-Wei Ke, Yu-Lun Liu
Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements. Residual priors trained on one geometry family frequently fail to beat parameter-free…
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…
TaRL: Learning General and Physical Rewards from Tactile Demonstrations
Po-Yi Wu, Dao-Jan Chang, Shang-Ya Hsiao +3
Contact-rich manipulation requires robots to sequence precise contacts, maintain stable grasps, and apply directed forces. Reinforcement learning (RL) can acquire such behaviors au…
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…