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Seyed Roozbeh Razavi Rohani

2 accepted papers

2025

Adapting to Frequent Human Direction Changes in Autonomous Frontal Following Robots

RA-L 2025

This letter addresses the challenge of robot follow ahead applications where the human behavior is highly variable. We propose a novel approach that does not rely on single human trajectory prediction but instead considers multiple potential future positions of the human, along with their associated

Cited by 5SourceScholar
2022

BIMRL: Brain Inspired Meta Reinforcement Learning

IROS 2022poster

Sample efficiency has been a key issue in reinforcement learning (RL). An efficient agent must be able to leverage its prior experiences to quickly adapt to similar, but new tasks and situations. Meta-RL is one attempt at formalizing and ad-dressing this issue. Inspired by recent progress in meta-RL…

Cited by 9SourcecodeScholar