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相关论文: Socially Compliant Navigation Dataset (SCAND): A L…

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Navigation in human-robot shared crowded environments remains challenging, as robots are expected to move efficiently while respecting human motion conventions. However, many existing approaches emphasize safety or efficiency while…

机器人学 · 计算机科学 2025-06-18 Zhirui Sun , Xingrong Diao , Yao Wang , Bi-Ke Zhu , Jiankun Wang

Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end…

Reliable embodied perception from an egocentric perspective is challenging yet essential for autonomous navigation technology of intelligent mobile agents. With the growing demand of social robotics, near-field scene understanding becomes…

计算机视觉与模式识别 · 计算机科学 2025-03-06 Haisheng Su , Feixiang Song , Cong Ma , Wei Wu , Junchi Yan

Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengages the robot's autonomy system due to the robot performing…

机器人学 · 计算机科学 2020-10-12 Gregory Kahn , Pieter Abbeel , Sergey Levine

Navigating large-scale outdoor environments requires complex reasoning in terms of geometric structures, environmental semantics, and terrain characteristics, which are typically captured by onboard sensors such as LiDAR and cameras. While…

Navigation is a rich and well-grounded problem domain that drives progress in many different areas of research: perception, planning, memory, exploration, and optimisation in particular. Historically these challenges have been separately…

Mobile robots navigating in crowds trained using reinforcement learning are known to suffer performance degradation when faced with out-of-distribution scenarios. We propose that by properly accounting for the uncertainties of pedestrians,…

机器人学 · 计算机科学 2025-08-08 Jianpeng Yao , Xiaopan Zhang , Yu Xia , Zejin Wang , Amit K. Roy-Chowdhury , Jiachen Li

Navigating dynamic urban environments presents significant challenges for embodied agents, requiring advanced spatial reasoning and adherence to common-sense norms. Despite progress, existing visual navigation methods struggle in map-free…

计算机视觉与模式识别 · 计算机科学 2025-04-23 Xinhao Liu , Jintong Li , Yicheng Jiang , Niranjan Sujay , Zhicheng Yang , Juexiao Zhang , John Abanes , Jing Zhang , Chen Feng

This paper considers the problem of enabling robots to navigate dynamic environments while following instructions. The challenge lies in the combinatorial nature of instruction specifications: each instruction can include multiple…

For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with…

机器人学 · 计算机科学 2017-05-18 Anirudh Vemula , Katharina Muelling , Jean Oh

Recent protocols and metrics for training and evaluating autonomous robot navigation through crowds are inconsistent due to diversified definitions of "social behavior". This makes it difficult, if not impossible, to effectively compare…

机器人学 · 计算机科学 2022-11-29 Junxian Wang , Wesley P. Chan , Pamela Carreno-Medrano , Akansel Cosgun , Elizabeth Croft

Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the highly dynamic, partially observable environment. Occlusions are highly prevalent in such settings due to a limited sensor field of view and obstructing…

机器人学 · 计算机科学 2023-05-02 Ye-Ji Mun , Masha Itkina , Shuijing Liu , Katherine Driggs-Campbell

We present a Pedestrian Dominance Model (PDM) to identify the dominance characteristics of pedestrians for robot navigation. Through a perception study on a simulated dataset of pedestrians, PDM models the perceived dominance levels of…

机器人学 · 计算机科学 2019-02-15 Tanmay Randhavane , Aniket Bera , Emily Kubin , Austin Wang , Kurt Gray , Dinesh Manocha

Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives…

Over the past decade, a multitude of service robots have been developed to fulfill a wide range of practical purposes. Notably, roles such as reception and robotic guidance have garnered extensive popularity. In these positions, robots are…

机器人学 · 计算机科学 2024-05-03 Andrea Ruo , Lorenzo Sabattini , Valeria Villani

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with…

机器学习 · 计算机科学 2021-08-23 Yuejiang Liu , Qi Yan , Alexandre Alahi

We present a real-time algorithm, SocioSense, for socially-aware navigation of a robot amongst pedestrians. Our approach computes time-varying behaviors of each pedestrian using Bayesian learning and Personality Trait theory. These…

机器人学 · 计算机科学 2017-06-06 Aniket Bera , Tanmay Randhavane , Rohan Prinja , Dinesh Manocha

In this paper, we consider the problem of designing collision-free, dynamically feasible, and socially-aware trajectories for robots operating in environments populated by humans. We define trajectories to be social-aware if they do not…

机器人学 · 计算机科学 2020-03-03 Xusheng Luo , Yan Zhang , Michael M. Zavlanos

The objective of this work is to expand upon previous works, considering socially acceptable behaviours within robot navigation and interaction, and allow a robot to closely approach static and dynamic individuals or groups. The space…

机器人学 · 计算机科学 2023-10-17 Ricarte Ribeiro , Plinio Moreno

Navigation functions provide both path and motion planning, which can be used to ensure obstacle avoidance and convergence in the sphere world. When dealing with complex and realistic scenarios, constructing a transformation to the sphere…

机器人学 · 计算机科学 2022-10-04 Li Fan , Jianchang Liu , Wenle Zhang , Peng Xu