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cs.RO2025
SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning
Taewook Nam, Junmo Cho, Youngsoo Jang +1
Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human opera…
cs.LG2025★ 1 cited
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety
Geon-Hyeong Kim, Yu Jin Kim, Byoungjip Kim +4
As Large Language Models (LLMs) are increasingly deployed in real-world applications, balancing helpfulness and safety has become a central challenge. A natural approach is to inco…