Occlusion-Aware Crowd Navigation Using People as Sensors

Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the highly dynamic, partially observable environment. Occlusions are highly prevalent in such settings due to a limited sensor field of view and obstructing human agents. Previous work has shown that observed interactive behaviors of human agents can be used to estimate potential obstacles despite occlusions. We propose integrating such social inference techniques into the planning pipeline. We use a variational autoencoder with a specially designed loss function to learn representations that are meaningful for occlusion inference. This work adopts a deep reinforcement learning approach to incorporate the learned representation for occlusion-aware planning. In simulation, our occlusion-aware policy achieves comparable collision avoidance performance to fully observable navigation by estimating agents in occluded spaces. We demonstrate successful policy transfer from simulation to the real-world Turtlebot 2i. To the best of our knowledge, this work is the first to use social occlusion inference for crowd navigation.Comment: 7 pages, 4 figure

Motion Synthesis and Control for Autonomous Agents using Generative Models and Reinforcement Learning

Imitating and predicting human motions have wide applications in both graphics and robotics, from developing realistic models of human movement and behavior in immersive virtual worlds and games to improving autonomous navigation for service agents deployed in the real world. Traditional approaches for motion imitation and prediction typically rely on pre-defined rules to model agent behaviors or use reinforcement learning with manually designed reward functions. Despite impressive results, such approaches cannot effectively capture the diversity of motor behaviors and the decision making capabilities of human beings. Furthermore, manually designing a model or reward function to explicitly describe human motion characteristics often involves laborious fine-tuning and repeated experiments, and may suffer from generalization issues. In this thesis, we explore data-driven approaches using generative models and reinforcement learning to study and simulate human motions. Specifically, we begin with motion synthesis and control of physically simulated agents imitating a wide range of human motor skills, and then focus on improving the local navigation decisions of autonomous agents in multi-agent interaction settings. For physics-based agent control, we introduce an imitation learning framework built upon generative adversarial networks and reinforcement learning that enables humanoid agents to learn motor skills from a few examples of human reference motion data. Our approach generates high-fidelity motions and robust controllers without needing to manually design and finetune a reward function, allowing at the same time interactive switching between different controllers based on user input. Based on this framework, we further propose a multi-objective learning scheme for composite and task-driven control of humanoid agents. Our multi-objective learning scheme balances the simultaneous learning of disparate motions from multiple reference sources and multiple goal-directed control objectives in an adaptive way, enabling the training of efficient composite motion controllers. Additionally, we present a general framework for fast and robust learning of motor control skills. Our framework exploits particle filtering to dynamically explore and discretize the high-dimensional action space involved in continuous control tasks, and provides a multi-modal policy as a substitute for the commonly used Gaussian policies. For navigation learning, we leverage human crowd data to train a human-inspired collision avoidance policy by combining knowledge distillation and reinforcement learning. Our approach enables autonomous agents to take human-like actions during goal-directed steering in fully decentralized, multi-agent environments. To inform better control in such environments, we propose SocialVAE, a variational autoencoder based architecture that uses timewise latent variables with socially-aware conditions and a backward posterior approximation to perform agent trajectory prediction. Our approach improves current state-of-the-art performance on trajectory prediction tasks in daily human interaction scenarios and more complex scenes involving interactions between NBA players. We further extend SocialVAE by exploiting semantic maps as context conditions to generate map-compliant trajectory prediction. Our approach processes context conditions and social conditions occurring during agent-agent interactions in an integrated manner through the use of a dual-attention mechanism. We demonstrate the real-time performance of our approach and its ability to provide high-fidelity, multi-modal predictions on various large-scale vehicle trajectory prediction tasks

Integrating Perception, Prediction and Control for Adaptive Mobile Navigation

Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments promise to bring robotic assistance closer to our daily lives. A key limitation of existing navigation policies is the difficulty to predict and reason about the environment including static obstacles and pedestrians. In this thesis, I explore three properties of navigation including prediction of occupied spaces, prediction of pedestrians and measurements of uncertainty to improve crowd-based navigation. The hypothesis is that improving prediction and uncertainty estimation will increase robot navigation performance resulting in fewer collisions, faster speeds and lead to more socially-compliant motion in crowds. Specifically, this thesis focuses on techniques that allow mobile robots to predict occupied spaces that extend beyond the line of sight of the sensor. This is accomplished through the development of novel generative neural network architectures that enable map prediction that exceed the limitations of the sensor. Further, I extend the neural network architectures to predict multiple hypotheses and use the variance of the hypotheses as a measure of uncertainty to formulate an information-theoretic map exploration strategy. Finally, control algorithms that leverage the predicted occupancy map were developed to demonstrate more robust, high-speed navigation on a physical small form factor autonomous car. I further extend the prediction and uncertainty approaches to include modeling pedestrian motion for dynamic crowd navigation. This includes developing novel techniques that model human intent to predict future motion of pedestrians. I show this approach improves state-of-the-art results in pedestrian prediction. I then show errors in prediction can be used as a measure of uncertainty to adapt the risk sensitivity of the robot controller in real time. Finally, I show that the crowd navigation algorithm extends to socially compliant behavior in groups of pedestrians. This research demonstrates that combining obstacle and pedestrian prediction with uncertainty estimation achieves more robust navigation policies. This approach results in improved map exploration efficiency, faster robot motion, fewer number of collisions and more socially compliant robot motion within crowds

1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey

Scenario generation is one of the essential steps in scenario-based testing and, therefore, a significant part of the verification and validation of driver assistance functions and autonomous driving systems. However, the term scenario generation is used for many different methods, e.g., extraction of scenarios from naturalistic driving data or variation of scenario parameters. This survey aims to give a systematic overview of different approaches, establish different categories of scenario acquisition and generation, and show that each group of methods has typical input and output types. It shows that although the term is often used throughout literature, the evaluated methods use different inputs and the resulting scenarios differ in abstraction level and from a systematical point of view. Additionally, recent research and literature examples are given to underline this categorization.Comment: accepted at IEEE IV 202

Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving

Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledge about other agents behaviors and use that to forecast what might happen in the future. Inspired by humans, we provide our AVs the capability of anticipating future states and leveraging prediction in a cooperative reinforcement learning (RL) decision-making framework, to improve safety and robustness. In this paper, we propose an integration of two essential and earlier-presented components of AVs: social navigation and prediction. We formulate the AV decision-making process as a RL problem and seek to obtain optimal policies that produce socially beneficial results utilizing a prediction-aware planning and social-aware optimization RL framework. We also propose a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the value function network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the interpretable kinematic predictions to mask the unsafe actions, constraining the RL policy. We compare our prediction-aware AV to state-of-the-art solutions and demonstrate performance improvements in terms of efficiency and safety in multiple simulated scenarios

深層強化学習を用いた動的環境下における事前知識不要なロボットナビゲーションに関する研究

Tohoku University博士（工学）thesi

Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning

The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics.Comment: TSAS Submissio

Trajectory similarity learning with auxiliary supervision and optimal matching

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