2 papers
cs.RO2026
Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation
Zeyi Liu, Guangyao Liu, Yinuo Qu +6
Human-in-the-loop reinforcement learning (HIL-RL) improves sample efficiency in real-robot manipulation through online human intervention. However, successful trajectories may incl…
cs.LG2026
Consistency-Driven Calibration and Matching for Few-Shot Class-Incremental Learning
Qinzhe Wang, Zixuan Chen, Keke Huang +3
Few-Shot Class Incremental Learning (FSCIL) is crucial for adapting to the complex open-world environments. Contemporary prospective learning-based space construction methods strug…