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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 robots own state as input and outputs steering control. The laser sequence is further transformed into stacked local obstacle maps disentangled from robots ego motion to separate the static and dynamic obstacles, simplifying the model training. We observe that our method can be trained with significantly less real interaction data in simulator but achieve similar level of success rate in social navigation task compared with other methods. Experiments were conducted in multiple social scenarios both in simulation and on real robots, the learned policy can guide the robots to the final targets successfully while avoiding pedestrians in a socially compliant manner. Code is available at https://github.com/YuxiangCui/model-based-social-navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps s
Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. Ho
We present CoMet, a novel approach for computing a groups cohesion and using that to improve a robots navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by ut
Robot navigation in a safe way for complex and crowded situations is studied in this work. When facing complex environments with both static and dynamic obstacles, in existing works unicycle nonholonomic robots are prone to two extreme behaviors, one
Recent literature in the robotics community has focused on learning robot behaviors that abstract out lower-level details of robot control. To fully leverage the efficacy of such behaviors, it is necessary to select and sequence them to achieve a giv