https://github.com/Oghma/sns-lstm
공식 코드Social and Scene-Aware Trajectory Prediction in Crowded Spaces
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
This task studies how to navigate robot(s) among humans in a safe and socially acceptable way.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Social and Scene-Aware Trajectory Prediction in Crowded Spaces
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
From Cognition to Precognition: A Future-Aware Framework for Social Navigation
Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
Hyp2Nav: Hyperbolic Planning and Curiosity for Crowd Navigation
SocNavGym: A Reinforcement Learning Gym for Social Navigation
HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
Learning Social Cost Functions for Human-Aware Path Planning
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
Visual Navigation Among Humans with Optimal Control as a Supervisor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.