This paper by proposing the Human-in-Loop Policy Search (HILPS) framework, where learning from demonstration, learning from human intervention and Near Optimal ...
To verify the performance of the algorithm, the mobile robot navigation experiments are extensively conducted in simulation and real world. Results show that ...
We developed an autonomous mobile robot system based on behaviors acquired by deep reinforcement learning. Navigation performances including traveling ...
Mar 23, 2016 · A system is proposed to solve mobile robot navigation by opting for the most popular two RL algorithms, Sarsa and Q, and uses state and ...
This research focuses on how Large Language Models (LLMs) can help with (path) planning for mobile embodied agents such as robots, in a human-in-the-loop and ...
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HILPS: Human-in-Loop Policy Search for Mobile Robot Navigation. Conference Paper. Dec 2020. Mingxing Wen · Yufeng Yue · Zhenyu Wu ...
A systematic review on recent advances in autonomous mobile ...
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The purpose of this paper is to provide a comprehensive and up-to-date literature review of the path planning strategies that have received a considerable ...
Abstract. Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering.
Mar 22, 2024 · In this paper, we propose a novel hybrid approach called Social Robot Planner (SRLM), which integrates Large Language Models (LLM) and Deep Reinforcement ...
Missing: HILPS: Search
This thesis proposes to increase the efficiency of the human-robot navigation system via increasing the autonomy of the robot in general robot navigation, ...
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