Chrome Extension
WeChat Mini Program
Use on ChatGLM

Asymmetric Self-Play for Learning Robust Human-Robot Interaction on Crowd Navigation Tasks

2022 3rd International Conference on Electronics, Communications and Information Technology (CECIT)(2022)

School of Data Science | School of Computer Science and Technology

Cited 0|Views21
Abstract
A robot can make a sound to aware nearby pedestrians during its navigation, which often results in a more efficient and safer trajectory in a crowded environment. However, it is challenging to integrate such interaction capability into an existing robot navigation policy. In this paper, we propose a deep reinforcement learning (DRL) approach for integration, which results in effective interactive robot navigation policies in crowded environments. In particular, we specify the interaction capability by a set of high-level actions with flexible control. Then the navigation policy can be considered as a specific implementation of the ‘move’ action. Based on these high-level actions, we can train a robust human-robot interaction policy via asymmetric self-play, where the robot and some pedestrians, considered as naughty kids, play a game. Then the interaction policy can not only interact with pedestrians who cooperate with the robot but also with pedestrians who try to block the robot. We evaluate our approach in various simulation environments and compare it with multiple approaches. The experimental results show that our approach is robust and performs well in dense environments that are challenging for others. We also deploy the trained policy on a robot and evaluate its performance in multiple real-world crowded environments. A demonstration video is available online at https://youtu.be/x32YmivsIh0.
More
Translated text
Key words
deep reinforcement learning,self-play,parameterized action space,crowd navigation
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
D HELBING, P MOLNAR
1995

被引用8417 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest