Stefan Zickler

Learn More
We develop an intermediate representation for deformable part models and show that this representation has favorable performance characteristics for multi-class problems when the number of classes is high. Our model uses sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. This leads to a(More)
The current RoboCup Small Size League rules allow every team to set up their own global vision system as a primary sensor. This option, which is used by all participating teams, bears several organizational limitations and thus impairs the league’s progress. Additionally, most teams have converged on very similar solutions, and have produced only few(More)
Motion planning in dynamic environments consists of the generation of a collision-free trajectory from an initial to a goal state. When the environment contains uncertainty, preventing a perfect predictive model of its dynamics, a robot ends up only successfully executing a short part of the plan and then requires replanning, using the latest observed state(More)
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(More)
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(More)
Motion planning for mobile agents, such as robots, acting in the physical world is a challenging task, which traditionally concerns safe obstacle avoidance. We are interested in physics-based planning beyond collision-free navigation goals, in which the agent also needs to achieve its goals, including purposefully manipulate non-actuated bodies, in(More)
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(More)
Traditional motion planning focuses on the problem of safely navigating a robot through an obstacle-ridden environment. In this thesis, we address the question of how to perform robot motion planning in complex domains, with goals that go beyond collision-free navigation. Specifically, we are interested in problems that impose challenging constraints on the(More)