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Kenzo Lobos-Tsunekawa

2 accepted papers

2020

Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation

IROS 2020poster

Reinforcement Learning (RL), among other learning-based methods, represents powerful tools to solve complex robotic tasks (e.g., actuation, manipulation, navigation, etc.), with the need for real-world data to train these systems as one of its most important limitations. The use of simulators is one…

Cited by 13SourceScholar
2018

Visual Navigation for Biped Humanoid Robots Using Deep Reinforcement Learning

RA-L 2018

In this letter, we propose a map-less visual navigation system for biped humanoid robots, which extracts information from color images to derive motion commands using deep reinforcement learning (DRL). The map-less visual navigation policy is trained using the Deep Deterministic Policy Gradients (DD

Cited by 94SourceScholar