Human Motion Trajectory Prediction: A Survey

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

Learn to Grasp via Intention Discovery and its Application to Challenging Clutter

Humans excel in grasping objects through diverse and robust policies, many of which are so probabilistically rare that exploration-based learning methods hardly observe and learn. Inspired by the human learning process, we propose a method to extract and exploit latent intents from demonstrations, and then learn diverse and robust grasping policies through self-exploration. The resulting policy can grasp challenging objects in various environments with an off-the-shelf parallel gripper. The key component is a learned intention estimator, which maps gripper pose and visual sensory to a set of sub-intents covering important phases of the grasping movement. Sub-intents can be used to build an intrinsic reward to guide policy learning. The learned policy demonstrates remarkable zero-shot generalization from simulation to the real world while retaining its robustness against states that have never been encountered during training, novel objects such as protractors and user manuals, and environments such as the cluttered conveyor.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

Transformation of Attributed Structures with Cloning (Long Version)

Copying, or cloning, is a basic operation used in the specification of many applications in computer science. However, when dealing with complex structures, like graphs, cloning is not a straightforward operation since a copy of a single vertex may involve (implicitly)copying many edges. Therefore, most graph transformation approaches forbid the possibility of cloning. We tackle this problem by providing a framework for graph transformations with cloning. We use attributed graphs and allow rules to change attributes. These two features (cloning/changing attributes) together give rise to a powerful formal specification approach. In order to handle different kinds of graphs and attributes, we first define the notion of attributed structures in an abstract way. Then we generalise the sesqui-pushout approach of graph transformation in the proposed general framework and give appropriate conditions under which attributed structures can be transformed. Finally, we instantiate our general framework with different examples, showing that many structures can be handled and that the proposed framework allows one to specify complex operations in a natural way

`Human clones talk about their lives': Media representations of assisted reproductive and biogenetic technologies

This article examines New Zealand print media representations of assisted reproductive and related biogenetic technologies, conceptualized as the products of a concordance of interest between media workers and reproductive specialists, biogenetic scientists and consumers. Such concordance is evident in the predominant use of media frames of anecdotal personalization and technoboosterism, which typically amplify the voices of proponents of emerging technologies while marginalizing and delegitimizing counterdiscourses. Thus, the perspectives of consumers and 'expert' sources are privileged at the expense of a more balanced assessment of the value and social, ethical, legal and health implications of assisted reproductive and related biogenetic technologies. Source dependence also detracts from much-needed recognition of the professional and financial interests at stake in the growing privatization and commercialization of these technologies, and in the local context potentially undermines journalistic independence and integrity