We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesian-inference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot.
We present CoMet, a novel approach for computing a group's cohesion and using that to improve a robot's navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board a robot. Specifically, we detect characteristics corresponding to proximity between people, their relative walking speeds, the group size, and interactions between group members. We use our cohesion-metric to design and improve a navigation scheme that accounts for diffe…
Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
Department of Robotics, University of Michigan（机器人学系，密歇根大学）；Toyota North America Research & Development（丰田北美研发）；Department of Robotics and the Department of Aerospace Engineering at University of Michigan（机器人学系和航空航天工程系，密歇根大学）
Southern University of Science and Technology（南方科技大学）；Shandong University（山东大学）；Shenzhen Key Laboratory of Robotics Perception and Intelligence（深圳市机器人感知与智能重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute of The Chinese University of Hong Kong（香港中文大学深圳研究院）
Mobile robots have become more and more popular in large-scale and crowded environments, such as airports, shopping malls, etc. However, due to sparse landmarks and crowd noise, localization in this environment is a great challenge. Furthermore, it is unreliable for the robot to navigate safely in crowds while considering human comfort. Thus, how to navigate safely with localization precision in that environment is a critical problem. To solve this problem, we proposed a curiosity-based framework that can find an effective path with the consideration of human comfort and crowds, localization u…
The Graduate Center（纽约市立大学研究生中心）；Hunter College of The City University of New York（纽约市立大学亨特学院）
While effective navigation in large, crowded environments is essential for an autonomous robot, preliminary testing of algorithms to support it requires simulation across a broad range of crowd scenarios. Most available simulation tools provide either realistic crowds without robots or realistic robots without realistic crowds. This paper introduces MengeROS, a 2-D simulator that realistically integrates multiple robots and crowds. MengeROS provides a broad range of settings in which to test the capabilities and performance of navigation algorithms designed for large crowded environments.
Risky and crowded environments (RCE) contain abstract sources of risk and uncertainty, which are perceived differently by humans, leading to a variety of behaviors. Thus, robots deployed in RCEs, need to exhibit diverse perception and planning capabilities in order to interpret other human agents' behavior and act accordingly in such environments. To understand this problem domain, we conducted a study to explore human path choices in RCEs, enabling better robotic navigational explainable AI (XAI) designs. We created a novel COVID-19 pandemic grocery shopping scenario which had time-risk trade…
Robot navigation in human semi-static and crowded environments can lead to the freezing problem, where the robot can not move due to the presence of humans standing on its path and no other path is available. Classical approaches of robot navigation do not provide a solution for this problem. In such situations, the robot could interact with the humans in order to clear its path instead of considering them as unanimated obstacles. In this work, we propose a robot behavior for a wheeled humanoid robot that complains with social norms for clearing its path when the robot is frozen due to the pre…
Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding dynamics of mobile robots. The model takes laser scan sequence and robot's own state as input and outputs steering control. The laser sequence is further transformed into stacked local obstacle maps …
We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich crowd-robot demonstrations. While there has been important progress on models for offline pedestrian motion forecasting, transferring their performance on real robots is nontrivial due to close interaction settings and novelty effects on users. In this paper, we investigate the utility of a recent stat…
Baidu Research, Baidu Inc.（百度公司百度研究院）；City University of Hong Kong（香港城市大学）；University of Maryland, College Park（马里兰大学帕克分校）
Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.
Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, multi-subgoal robot navigation approach based on deep reinforcement learning is proposed, which can reason about more comprehensive relationships among all agents (robot and humans). Specifically, the next position point is planned for the robot by introducing history information and interactions in our work. Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interac…
Safe mobility support system using crowd mapping and avoidance route planning using VLM
Graduate School of Regional Development and Creativity, Division of Engineering and Agriculture, Graduate School, Utsunomiya University（乌市大学研究生院区域发展与创意学院，工程与农业系）
This work presents a deep reinforcement learning framework for interactive navigation in a crowded place. Our proposed approach, Learning to Balance (L2B) framework enables mobile robot agents to steer safely towards their destinations by avoiding collisions with a crowd, while actively clearing a path by asking nearby pedestrians to make room, if necessary, to keep their travel efficient. We observe that the safety and efficiency requirements in crowd-aware navigation have a trade-off in the presence of social dilemmas between the agent and the crowd. On the one hand, intervening in pedestria…
Logic-Guided Socially-aware Robot Navigation World Model
School of Applied and Creative Computing, Purdue University（应用与创意计算学院，普渡大学）；Department of Computer Science, Purdue University（计算机科学系，普渡大学）；Department of Applied Artificial Intelligence, Sungkyunkwan University（应用人工智能系，成均馆大学）；Department of Computer Science and Department of Intelligent Systems Engineering, Indiana University Bloomington（计算机科学系和智能系统工程系，印第安纳大学布卢明顿分校）
In recent years, great efforts have been devoted to deep imitation learning for autonomous driving control, where raw sensory inputs are directly mapped to control actions. However, navigating through densely populated intersections remains a challenging task due to uncertainty caused by uncertain traffic participants. We focus on autonomous navigation at crowded intersections that require interaction with pedestrians. A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction. A new benchmark called…
Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emerged as an alternative planning method to conservative approaches and promises more efficient and flexible navigation. However, in highly dynamic environments employing different kinds of obstacle classes, safe navigation still presents a grand challenge. In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstac…
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning
The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifie…
Large efforts have focused on ensuring that the controllers for mobile service robots follow proxemics and other social rules to ensure both safe and socially acceptable distance to pedestrians. Nonetheless, involuntary contact may be unavoidable when the robot travels in crowded areas or when encountering adversarial pedestrians. Freezing the robot in response to contact might be detrimental to bystanders' safety and prevents it from achieving its task. Unavoidable contacts must hence be controlled to ensure the safe and smooth travelling of robots in pedestrian alleys. We present a force-lim…
Navigating the Crowd: Non-linear MPC with Social Forces Dynamics for Human-Aware Robot Navigation
Department of Electronics and Telecommunications, Politecnico di Torino（电子与电信系，都灵理工大学）；School of Engineering, Pablo de Olavide University（工程学院，巴勃罗·德奥拉维德大学）
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy in trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in …
Nuro, Inc.（Nuro公司）；University of Pennsylvania（宾夕法尼亚大学）
Autonomous navigation in crowded, complex urban environments requires interacting with other agents on the road. A common solution to this problem is to use a prediction model to guess the likely future actions of other agents. While this is reasonable, it leads to overly conservative plans because it does not explicitly model the mutual influence of the actions of interacting agents. This paper builds a reinforcement learning-based method named MIDAS where an ego-agent learns to affect the control actions of other cars in urban driving scenarios. MIDAS uses an attention-mechanism to handle an…
When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new algorithms that can take fully advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data in the first place. To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of targets (not just pedestrians, but also biker…
Maintaining social distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not adhering to the social distance constraint, i.e. about 6 feet of space between them. Our approach makes no assumption about the crowd density or pedestrian walking directions. We use a mobile robot with commodity sensors, namely an RGB-D camera and a 2-D lidar to perform collision-free navigation in a crowd and estimate the distance between all detected individuals…
Learning to Drive Anywhere with Model-Based Reannotation
Department of Electrical Engineering and Computer Sciences , University of California, Berkeley, CA USA（电气工程与计算机科学系，加州大学伯克利分校）；Toyota Motor North America, Inc（丰田北美公司）；Department of Electrical and Computer Engineering, Princeton University（电气与计算机工程系，普林斯顿大学）
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…
