Collaborating Authors

Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning Artificial Intelligence

Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environments and environments with dense crowds. To tackle these problems, we propose decentralized structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We train our network with model-free deep reinforcement learning without any expert supervision. We demonstrate that our model outperforms previous methods and successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i.

Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze Artificial Intelligence

Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze Y uying Chen, Congcong Liu, Ming Liu, Bertram E. Shi Abstract -- Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd. Then we incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-of- art methods by 18.4% in task accomplishment and by 16.4% in time efficiency. I NTRODUCTION With the rapid development of artificial intelligence technologies, mobile robot navigation has many vital applications in crowded pedestrian environments such as hospitals, shopping malls, and canteens. In these scenarios with dense crowds, navigating robots safely and efficiently is a crucial, yet still challenging, problem [1]. Traditional approaches often treat pedestrians as simple dynamic obstacles and focus only on the next step [2], [3]. Since these approaches do not model human behavior, they result in behavior that can seem unnatural and shortsighted.

An End-to-End Learning Approach for Trajectory Prediction in Pedestrian Zones Artificial Intelligence

This paper aims to explore the problem of trajectory prediction in heterogeneous pedestrian zones, where social dynamics representation is a big challenge. Proposed is an end-to-end learning framework for prediction accuracy improvement based on an attention mechanism to learn social interaction from multi-factor inputs.

Social navigation with human empowerment driven reinforcement learning Artificial Intelligence

The next generation of mobile robots needs to be socially-compliant to be accepted by humans. As simple as this task may seem, defining compliance formally is not trivial. Yet, classical reinforcement learning (RL) relies upon hard-coded reward signals. In this work, we go beyond this approach and provide the agent with intrinsic motivation using empowerment. Empowerment maximizes the influence of an agent on its near future and has been shown to be a good model for biological behaviors. It also has been used for artificial agents to learn complicated and generalized actions. Self-empowerment maximizes the influence of an agent on its future. On the contrary, our robot strives for the empowerment of people in its environment, so they are not disturbed by the robot when pursuing their goals. We show that our robot has a positive influence on humans, as it minimizes the travel time and distance of humans while moving efficiently to its own goal. The method can be used in any multi-agent system that requires a robot to solve a particular task involving humans interactions.

DeepSocNav: Social Navigation by Imitating Human Behaviors Artificial Intelligence

Current datasets to train social behaviors are usually borrowed from surveillance applications that capture visual data from a bird's-eye perspective. This leaves aside precious relationships and visual cues that could be captured through a first-person view of a scene. In this work, we propose a strategy to exploit the power of current game engines, such as Unity, to transform pre-existing bird's-eye view datasets into a first-person view, in particular, a depth view. Using this strategy, we are able to generate large volumes of synthetic data that can be used to pre-train a social navigation model. To test our ideas, we present DeepSocNav, a deep learning based model that takes advantage of the proposed approach to generate synthetic data. Furthermore, DeepSocNav includes a self-supervised strategy that is included as an auxiliary task. This consists of predicting the next depth frame that the agent will face. Our experiments show the benefits of the proposed model that is able to outperform relevant baselines in terms of social navigation scores.