Accurate real-time person detection is an important capability for many robot tasks, such as indoor navigation and human-robot interaction. In this paper, we introduce a depth-augmented, GPU-accelerated version of Deformable Parts Models (DPM) that uses a joint RGB+Depth feature descriptor to perform high-accuracy person detection at 5Hz while requiring less than 10 Watts on a single 2014 consumer-grade embedded chip. We provide a detailed description of the algorithm and evaluate its speed/accuracy trade-offs on an indoor person detection dataset collected from a mobile platform, showing that our RGBD approach outperforms accuracy of RGB-only DPM, depth-only DPM, and RGB HOG SVM classifier cascades. We furthermore demonstrate how reductions in model complexity and feature space dimensionality can increase speed without significantly sacrificing detector accuracy.

The problem of real-time multiclass object recognition is of great practical importance in object recognition. In this paper, we describe a framework that simultaneously utilizes shared representation, reconstruction sparsity, and parallelism to enable real-time multiclass object detection with deformable part models at 5Hz on a laptop computer with almost no decrease in task performance. Our framework is trained in the standard structured output prediction formulation and is generically applicable for speeding up object recognition systems where the computational bottleneck is in multiclass, multi-convolutional inference. We experimentally demonstrate the efficiency and task performance of our method on PASCAL VOC, subset of ImageNet, Caltech101 and Caltech256 dataset.

The LANdroids project requires robots to autonomously localize, track, and follow (a task also known as tethering) other robots or humans in an unknown environment with limited sensing abilities. In this paper, we present a localization and tethering approach that relies solely on wireless signal strength and robot odometry without requiring any known reference points in the domain. We introduce a data-driven, probabilistic model that maps received signal strength (RSS) values to real-world distance distributions and embed this model in a grid-based localization algorithm that successfully performs the LANdroids tethering task. We furthermore show, that it is possible to improve localization through the addition of a compass sensor and inter-robot information sharing.

Abstract Controlling rigid body dynamic simulations can pose a difficult challenge when constraints exist on the bodies' goal states and the sequence of intermediate states in the resulting animation. Manually adjusting individual rigid body control actions (forces and torques) can become a very labour‐intensive and non‐trivial task, especially if the domain includes a large number of bodies or if it requires complicated chains of inter‐body collisions to achieve the desired goal state. Furthermore, there are some interactive applications that rely on rigid body models where no control guidance by a human animator can be offered at runtime, such as video games. In this work, we present techniques to automatically generate intelligent control actions for rigid body simulations. We introduce sampling‐based motion planning methods that allow us to model goal‐driven behaviour through the use of non‐deterministic Tactics that consist of intelligent, sampling‐based control‐blocks, called Skills. We introduce and compare two variations of a Tactics‐driven planning algorithm, namely behavioural Kinodynamic Rapidly Exploring Random Trees (BK‐RRT) and Behavioural Kinodynamic Balanced Growth Trees (BK‐BGT). We show how our planner can be applied to automatically compute the control sequences for challenging physics‐based domains and that is scalable to solve control problems involving several hundred interacting bodies, each carrying unique goal constraints.

After several years of developing multiple RoboCup small-size robot soccer teams, our CMDragons robot team achieved a highly successful level of performance, winning both the 2006 and 2007 competitions without losing a single game. Our small-size team consists of five executing wheeled robots with centralized, off-board perception and decision making. The decision making framework consists of a set of layered components, consisting of perception, evaluation and strategy, robot tactics and skills, and real-time navigation. In this paper, we present the strategy, action selection, and execution aspects of our architecture, with a focus on passing as an example of effective coordinated teamwork. The design enabled our robot team to score using multiple methods, from direct shooting up to 3D passes deflected in midair, resulting in a rich set of actions that were difficult for adversaries to counter. We provide several performance quantified claims supported by testing in our laboratory and in competition settings.