3,745 research outputs found

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

    Social Attention: Modeling Attention in Human Crowds

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    Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd

    Building Machines That Learn and Think Like People

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    Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

    From visuomotor control to latent space planning for robot manipulation

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    Deep visuomotor control is emerging as an active research area for robot manipulation. Recent advances in learning sensory and motor systems in an end-to-end manner have achieved remarkable performance across a range of complex tasks. Nevertheless, a few limitations restrict visuomotor control from being more widely adopted as the de facto choice when facing a manipulation task on a real robotic platform. First, imitation learning-based visuomotor control approaches tend to suffer from the inability to recover from an out-of-distribution state caused by compounding errors. Second, the lack of versatility in task definition limits skill generalisability. Finally, the training data acquisition process and domain transfer are often impractical. In this thesis, individual solutions are proposed to address each of these issues. In the first part, we find policy uncertainty to be an effective indicator of potential failure cases, in which the robot is stuck in out-of-distribution states. On this basis, we introduce a novel uncertainty-based approach to detect potential failure cases and a recovery strategy based on action-conditioned uncertainty predictions. Then, we propose to employ visual dynamics approximation to our model architecture to capture the motion of the robot arm instead of the static scene background, making it possible to learn versatile skill primitives. In the second part, taking inspiration from the recent progress in latent space planning, we propose a gradient-based optimisation method operating within the latent space of a deep generative model for motion planning. Our approach bypasses the traditional computational challenges encountered by established planning algorithms, and has the capability to specify novel constraints easily and handle multiple constraints simultaneously. Moreover, the training data comes from simple random motor-babbling of kinematically feasible robot states. Our real-world experiments further illustrate that our latent space planning approach can handle both open and closed-loop planning in challenging environments such as heavily cluttered or dynamic scenes. This leads to the first, to our knowledge, closed-loop motion planning algorithm that can incorporate novel custom constraints, and lays the foundation for more complex manipulation tasks

    Online Structure, Parameter, and Utility Updating of Bayesian Decision Networks for Cooperative Decision-Theoretic Agents

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    Multi-agent systems, systems consisting of more than one acting and decision making entities, are of great interest to researchers because they have advantages for some specific tasks where it would be more effective to use multiple small and simple robots rather than a large and complex one. One of the major problems with multi-agent systems is developing a means to organize or control the overall behavior of the system. Typically, multi-agent control involves one of two structures. In some designs, there is a hierarchy with some robots being leaders and other followers. Other designs involve robot specialization towards one particular task or individual robots which loosely or strongly cooperate in some manner to yield the desired behavior. This thesis studies using bayesian decision networks (BDNs) as a method to control individual robots to achieve some group or cooperative behavior. BDNs are powerful tools enabling designers of intelligent agents to model the agent\u27s environment and the behavior of other agents without expert knowledge about a system. The probabilistic nature of these networks allows agents to learn about themselves and their environment by updating their bayesian network (BN) with new observations. While two methods of learning and responding to change in the environment with BNs, parameter learning and structure learning, have been studied by many researchers as a means to control a single robot or teams of robots, a third method, utility updating, has seen little study. This work is thus a novel study of BN control since it incorporates all three methods to develop a decision theoretic agent (DTA). The agent is applied to a modified version of a personal rapid transit (PRT) problem (or personal automated transport (PAT)) that is simulated in Matlab. PRT is a proposed public transport method which offers automated on-demand transportation between any two nodes of the transportation network. The PRT problem of interest is that of autonomous control. This can be likened to one of multi-agent control of many identical agents. Several agents are developed to solve the problem, a rule based agent and BN-agents which use various subsets of the three network updating methods. The experimental results show that the DTA that uses parameter, structure, and utility updating could be a superior solution to agents based only on some subset of those methods
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