3 papers
cs.RO2026
ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
Nutan Chen, Jianxiang Feng, Marvin Alles +1
In recent years Flow Matching has become a prominent method for generative modeling robot motion generation. In its generic form Flow Matching is an ODE-based neural sampler that i…
cs.RO2026
SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty
Jongseok Lee, Ribin Balachandran, Harsimran Singh +6
Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical appl…
cs.RO2025
LensDFF: Language-enhanced Sparse Feature Distillation for Efficient Few-Shot Dexterous Manipulation
Qian Feng, David S. Martinez Lema, Jianxiang Feng +2
Learning dexterous manipulation from few-shot demonstrations is a significant yet challenging problem for advanced, human-like robotic systems. Dense distilled feature fields have…