Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

• URL: http://arxiv.org/abs/2306.16740v4
• Date: Tue, 19 Sep 2023 20:02:06 GMT
• Title: Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
• Authors: Anthony Francis (1), Claudia P\'erez-D'Arpino (2), Chengshu Li (3), Fei Xia (4), Alexandre Alahi (5), Rachid Alami (15), Aniket Bera (6), Abhijat Biswas (7), Joydeep Biswas (8), Rohan Chandra (8), Hao-Tien Lewis Chiang (4), Michael Everett (10), Sehoon Ha (11), Justin Hart (8), Jonathan P. How (9), Haresh Karnan (8), Tsang-Wei Edward Lee (4), Luis J. Manso (12), Reuth Mirksy (13), S\"oren Pirk (14), Phani Teja Singamaneni (15), Peter Stone (8,16), Ada V. Taylor (7), Peter Trautman (17), Nathan Tsoi (18), Marynel V\'azquez (18), Xuesu Xiao (19), Peng Xu (4), Naoki Yokoyama (11), Alexander Toshev (20), Roberto Mart\'in-Mart\'in (8) ((1) Logical Robotics, (2) NVIDIA, (3) Stanford, (4) Google, (5) EPFL, (6) Purdue, (7) CMU, (8) UT Austin, (9) MIT, (10) Northeastern, (11) Georgia Tech, (12) Aston, (13) Bar Ilan, (14) Adobe, (15) LAAS-CNRS, Universite de Toulouse, (16) Sony AI, (17) Honda, (18) Yale, (19) GMU, (20) Apple)
• Abstract summary: Social robot navigation is difficult to evaluate because it involves dynamic human agents and their perceptions of the appropriateness of robot behavior. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.
• Score: 44.51586279645062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.

Related papers

• Online Context Learning for Socially-compliant Navigation [49.609656402450746]
  This letter introduces an online context learning method that aims to empower robots to adapt to new social environments online. Experiments using a community-wide simulator show that our method outperforms the state-of-the-art ones.
  arXiv  Detail & Related papers  (2024-06-17T12:59:13Z)
• Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning [0.7864304771129751]
  Mobile robots are being used on a large scale in various crowded situations and become part of our society. Socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance. We propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans.
  arXiv  Detail & Related papers  (2024-03-14T18:25:40Z)
• Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments [3.714800947440209]
  The proposed method achieves significant improvements in social navigation metrics such as success rate, navigation time, and trajectory length. The concept of humans' awareness towards the robot is introduced into the social robot navigation framework.
  arXiv  Detail & Related papers  (2023-10-11T09:25:24Z)
• SACSoN: Scalable Autonomous Control for Social Navigation [62.59274275261392]
  We develop methods for training policies for socially unobtrusive navigation. By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. We collect a large dataset where an indoor mobile robot interacts with human bystanders.
  arXiv  Detail & Related papers  (2023-06-02T19:07:52Z)
• Learning Social Navigation from Demonstrations with Conditional Neural Processes [2.627046865670577]
  This paper presents a data-driven navigation architecture that uses Conditional Neural Processes to learn global and local controllers of the mobile robot from observations. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data.
  arXiv  Detail & Related papers  (2022-10-07T14:37:56Z)
• Gesture2Path: Imitation Learning for Gesture-aware Navigation [54.570943577423094]
  We present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control. We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios.
  arXiv  Detail & Related papers  (2022-09-19T23:05:36Z)
• Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation [92.66286342108934]
  Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a'socially compliant' manner in the presence of other intelligent agents such as humans. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of socially compliant, human teleoperated driving demonstrations.
  arXiv  Detail & Related papers  (2022-03-28T19:09:11Z)
• From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation [3.3511723893430476]
  We argue that social navigation models can replicate, promote, and amplify societal unfairness such as discrimination and segregation. Our proposed framework consists of two components: textitlearning which incorporates social context into the learning process to account for safety and comfort, and textitrelearning to detect and correct potentially harmful outcomes before the onset.
  arXiv  Detail & Related papers  (2021-01-07T17:42:35Z)
• ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation [65.11858854040543]
  We present ProxEmo, a novel end-to-end emotion prediction algorithm for robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation.
  arXiv  Detail & Related papers  (2020-03-02T17:47:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.