2 papers
cs.AI2026
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
Manith Adikari, Bei Peng, Samuele Vinanzi +1
Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function. However, real-world decision-making tasks often involve multiple, c…
cs.RO2026
Grounding Hierarchical Vision-Language-Action Models Through Explicit Language-Action Alignment
Theodor Wulff, Federico Tavella, Rahul Singh Maharjan +2
Achieving robot transparency is a critical step toward effective human-robot collaboration. To be transparent, a robot's natural language communication must be consistent with its…