Most path planning techniques use exact, global information of the environment to make optimal or near-optimal plans. In contrast, humans navigate using only local information, which they must augment with their understanding of typical building layouts to guess what lies ahead, while integrating what they have seen already to form mental representations of building structure. Here, we propose Scene Planning Networks (SPNets), a neural network based approach for formulating the long-range navigation problem as a series of local decisions similar to what humans face when navigating. Agents navigating using SPNets build additive neural representations of previous observations to understand local obstacle structure, and use a network-based planning approach to plan the next steps towards a fuzzy goal region. Our approach reproduces several important aspects of human behavior that are not captured by either full global planning or simple local heuristics.

With recent advances in hardware technology, autonomous robots are increasingly present in research activities outside of robotics, performing a multitude of tasks such as image capture and sample collection. However, user interfaces for task-oriented robots have not kept pace with hardware breakthroughs. Current planning and control interfaces for robots are not intuitive and often place a large cognitive burden on those who are not highly trained in their use. Augmented reality (AR) has also seen major advances in recent years. This demonstration illustrates an initial system design for an AR user interface for path planning with robotics.

Over the last two decades there has been a proliferation of methods for simulating crowds of humans. As the number of different methods and their complexity increases, it becomes increasingly unrealistic to expect researchers and users to keep up with all the possible options and trade-offs. We therefore see the need for tools that can facilitate both domain experts and non-expert users of crowd simulation in making high-level decisions about the best simulation methods to use in different scenarios. In this paper, we leverage trajectory data from human crowds and machine learning techniques to learn a manifold which captures representative local navigation scenarios that humans encounter in real life. We show the applicability of this manifold in crowd research, including analyzing trends in simulation accuracy, and creating automated systems to assist in choosing an appropriate simulation method for a given scenario.

Large multi-agent systems such as crowds involve inter-agent interactions that are typically anticipatory in nature, depending strongly on both the positions and the velocities of agents. We show how the nonlinear, anticipatory forces seen in multi-agent systems can be made compatible with recent work on energy-based formulations in physics-based animation, and propose a simple and effective optimization-based integration scheme for implicit integration of such systems. We apply this approach to crowd simulation by using a state-of-the-art model derived from a recent analysis of human crowd data, and adapting it to our framework. Our approach provides, for the first time, guaranteed collision-free motion while simultaneously maintaining high-quality collective behavior in a way that is insensitive to simulation parameters such as time step size and crowd density. These benefits are demonstrated through simulation results on various challenging scenarios and validation against real-world crowd data.

Animated digital characters play an important role in virtual experiences. In this work, we utilize data from a large scale user study as training data for a generative model for producing a variety of animated smiles. Our method involves a four stage process that samples a variety of facial expressions, and annotates them with perceived happiness from the user study. The expressions are then transformed into a standardized space and used by a non-parametric classifier to predict happiness of new smiles.