Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical \acf{RL} and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants.
BMW Group（宝马集团）；University of Amsterdam（阿姆斯特丹大学）；University of Hertfordshire（赫特福德大学）
We consider multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents can be brittle because they can overfit their training partners' policies. This overfitting can produce agents that adopt policies that act under the expectation that other agents will act in a certain way rather than react to their actions. Our objective is to bias the learning process towards finding reactive strategies towards other agents' behaviors. Our method, transfer empowerment, measures the potential influence between agents' actions. Results from three simulated co…
C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents
Faculty of Technology, Bielefeld University, Germany（比勒菲尔德大学技术学院，德国）；Department of Electrical Engineering and Information Technology, Paderborn University, Germany（帕德博恩大学电气工程与信息科技系，德国）
Social navigation has been gaining attentions with the growth in machine intelligence. Since reinforcement learning can select an action in the prediction phase at a low computational cost, it has been formulated in a social navigation tasks. However, reinforcement learning takes an enormous number of iterations until acquiring a behavior policy in the learning phase. This negatively affects the learning of robot behaviors in the real world. In particular, social navigation includes humans who are unpredictable moving obstacles in an environment. We proposed a reward shaping method with subgoa…
Universidade de Pernambuco（伯南布哥大学）；Istituto Italiano di Tecnologia（意大利理工学院）
Dealing with social tasks in robotic scenarios is difficult, as having humans in the learning loop is incompatible with most of the state-of-the-art machine learning algorithms. This is the case when exploring Incremental learning models, in particular the ones involving reinforcement learning. In this work, we discuss this problem and possible solutions by analysing a previous study on adaptive convolutional encoders for a social navigation task.
In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories. As mobile robots come to inhabit and traffic social spaces, they must account for social cues and behave in a socially compliant manner. We focus on learning such cues from examples. We describe an inverse reinforcement learning based algorithm which learns from human trajectory observations without knowing their specific actions. We increase the sample-efficiency of our approach over alternative methods by leveraging the notion of a replay buffer (found i…
University of Padova（帕多瓦大学）；Fondazione Bruno Kessler (FBK)（布鲁诺·凯斯勒基金会（FBK））；Simon Fraser University（西蒙弗雷泽大学）
Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agent to be integrated into our society. In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximity Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors. To this end, our tasks exploit the notion of immediate and future dangers of collision. Furthermore, we propose an evaluation protocol specifically designed for the Social Navigat…
We consider the problem of robust multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents, mainly those trained in a centralized way, can be brittle because they can adopt policies that act under the expectation that other agents will act a certain way rather than react to their actions. Our objective is to bias the learning process towards finding strategies that remain reactive towards others' behavior. Social empowerment measures the potential influence between agents' actions. We propose it as an additional reward term, so agents better a…
Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa（系统机器人研究所，理工学院，里斯本大学）；LAAS-CNRS, Artificial and Natural Intelligence Toulouse Institute (ANITI)（LAAS-CNRS，图卢兹人工智能研究所（ANITI））
Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration
School of Computer Science and Technology（计算机科学与技术学院）；Wuhan University of Science and Technology（武汉科技大学）；Hubei Province Key Laboratory of Intelligent Information Processing（湖北省智能信息处理重点实验室）
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）
In this paper is proposed an inclusion of the Social Force Model (SFM) into a concrete Deep Reinforcement Learning (RL) framework for robot navigation. These types of techniques have demonstrated to be useful to deal with different types of environments to achieve a goal. In Deep RL, a description of the world to describe the states and a reward adapted to the environment are crucial elements to get the desire behaviour and achieve a high performance. For this reason, this work adds a dense reward function based on SFM and uses the forces in the states like an additional description. Furthermo…
Massachusetts Institute of Technology（麻省理工学院）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging t…
This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the social navigation problem, our multi-modal learning planner explicitly considers social interaction factors, as well as social-awareness factors into T-MEDIRL pipeline to learn a reward function from human demonstrations. Moreover, we propose a novel trajectory ranking score using the sudden velocity change of pedestrians around the robot to address the sub-opti…
We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex environments, including doorways, hallways, intersections, and roundabouts. Unlike traditional simulators that concentrate on single robots with basic kinematic constraints in open spaces, SocialGym 2 employs multi-agent reinforcement learning (MARL) to develop optimal navigation policies for multiple robots with diverse, dynamic constraints in complex environments. Built on the PettingZoo MARL library and Stable Basel…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
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 psychological characteristics are used for long-term path prediction and generating proximic characteristics for each pedestrian. We combine these psychological constraints with social constraints to perform human-aware robot navigation in low- to medium-density crowds. The estimation of time-varying behaviors and pedestrian personalities can improve the performance…
SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation
Guohao School, Tongji University（同济大学国豪书院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能无人系统科学中心）；Tongji University（同济大学）；Western University（西安大略大学）；Aeromind Technology Co., Ltd.（深圳智元科技有限公司）；University at Buffalo-SUNY（纽约州立大学布法罗分校）
Socially-Aware Opinion-Based Navigation with Oval Limit Cycles
Università di Trento（特伦托大学）；Department of Industrial Engineering, Università di Trento（特伦托大学工业工程系）；Department of Information Engineering and Computer Science, Università di Trento（特伦托大学信息工程与计算机科学系）
