首页|New Data from Nanyang Technological University Illuminate Findings in Robotics (Learning Relation In Crowd Using Gated Graph Convolutional Networks for Drl-based Robot Navigation)
New Data from Nanyang Technological University Illuminate Findings in Robotics (Learning Relation In Crowd Using Gated Graph Convolutional Networks for Drl-based Robot Navigation)
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NETL
NSTL
IEEE
New research on Robotics is the subject of a report. According to news reporting originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Deep reinforcement learning (DRL) frameworks have shown their remarkable effectiveness in learning navigation policy for the mobile robot navigating in a human crowded environment. Moreover, attention mechanisms coupled with DRL allows the robot to identify neighbors with different level of influence and incorporate them into the robot’s decision.” Financial support for this research came from National Research Foundation, Singapore. Our news editors obtained a quote from the research from Nanyang Technological University, “However, as the crowd density increases, attention mechanisms may fail to identify critical neighbors which can lead to significant drops in navigation efficiency. In this work, we aim to address this limitation by encoding both human-human and human-robot interaction using a special class of Graph Convolutional Networks (GCN) known as Message-Passing GCN (MP-GCN). In contrast to existing methods, where attention between robot and humans are encoded uniformly, the proposed approach named MP-GatedGCN-RL encodes asymmetric interactions using the combination of novel message-passing function and edge-wise gating mechanisms. We evaluate our approach on the simulated environments of ETH/UCY pedestrians datasets consisting of different scenarios like collision avoidance, group forming, diverging, crossing, and so on. Experimental results demonstrate that our proposed method outperforms the conventional benchmark dynamic avoidance method ORCA with a 20.6% increase in success rate and a 9.1% reduction in navigation time.”
SingaporeSingaporeAsiaEmerging TechnologiesMachine LearningRobotRoboticsNanyang Technological University
NETL
NSTL
IEEE
