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cs.RO2026
Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization
Usman Nizamani, M. Shaheer Luqman, Fawad Javed Fateh +4
Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward-driven and often exhibit be…
cs.RO2026
A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics
Fawad Javed Fateh, Ali Shah Ali, Murad Popattia +4
We present a novel hierarchical spatiotemporal action tokenizer for in-context imitation learning. We first propose a hierarchical approach, which consists of two successive levels…