Projects at the RSE-lab

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RGB-D Kernel Descriptors

RGB-D Kernel descriptors is a general approach that extracts multi-level representations from high-dimensional structured data such as images, depth maps, and 3D point clouds.












Data-Efficient Robot Reinforcement Learning

Novel reinforcement learning methods that learn tasks in a few trials only and can run on real (non-simulated) robots in a reasonable amount of time.












Gambit Manipulator and Game Playing

The Gambit manipulator is a novel robotic arm combined with an RGBD camera, used for interacting dextrously with small-scale physical objects, as in game playing.












RGB-D Object Recognition and Pose Estimation

In this project we address joint object category, instance, and pose recognition in the context of rapid advances of RGB-D cameras that combine both visual and 3D shape information. The focus is on detection and classification of objects in indoor scenes, such as in domestic environments.












RGB-D Object Dataset

A large dataset of 300 common household objects recorded using a Kinect style 3D camera.












RGB-D Dense Point Cloud Mapping

We align RGB-D (Red, Green, Blue plus Depth) point clouds acquired with a depth camera to create globally consistent dense 3D maps.












Robotic In-Hand 3D Object Modeling

We address the problem of active object investigation using robotic manipulators and Kinect-style RGB-D depth sensors. To do so, we jointly tackle the issues of sensor to robot calibration, manipulator tracking, and 3D object model construction. We additionally consider the problem of motion and grasp planning to maximize coverage of the object.












Learning to Navigate Through Crowded Environments

In this project we use inverse reinforcement learning to train a planner for natural and efficient robotic motion in crowded environments.












GP-BayesFilters: Gaussian Process Bayes Filters for Dynamical Systems

The goal of this project is to integrate Gaussian process prediction and observation models into Bayes filters. These GP-BayesFilters are more accurate than standard Bayes filters using parametric models. In addition, GP models naturally supply the process and observation noise necessary for Bayesian filters.












Semantic Mapping

The goal of this project is to generate models that describe environments in terms of objects and places. Such representations contain far more useful information than traditional maps, and enable robots to interact with humans in a more natural way.












Activity Recognition

This project aims at learning and estimating high-level activities from raw sensor data. To do so, we strongly rely on the etimates generated by our people tracking approaches. We recently demonstrated that it is possible to learn typical outdoor navigation patterns of a person using raw GPS data. For example, our approach uses EM to learn where a person typically gets on or off the bus. Such techniques allow hand-held computer devices to assist people with cognitive disorders during their everyday life.










Particle Filters

With our collaborators, we introduced particle filters as a powerful tool for state estimation in mobile robotics. More recently, we developed several improvements to particle filters, including adaptive particle filters, which dynamically adapt the size of sample sets to the complexity of the underlying belief. We also developed real-time particle filters, which avoid loss of sensor information even under limited computational resources.





Mapping and Exploration

We are interested in the development of robust and efficient map buiding techniques. We developed different solutions to this problem, ranging from expectation maximization (EM) to Rao-Blackwellised particle filters. We also introduced novel coordination strategies for large teams of mobile robots. Within the CentiBots project, we developed a decision-theoretic approach that enables teams of robots to build a consistent map of an environment even when the robots start from different, completely unknown locations.







Active Sensing and Estimation in RoboCup

The task sounds simple: Program Sony AIBO robots to play soccer. We use RoboCup to investigate techniques for multi-robot collaboration, active sensing, and efficient state estimation. Our multi-model technique for ball tracking allows our robots to accurately track the ball and its interactions with the environment; even under the highly non-linear dynamics typically occuring during a soccer game. Our active sensing strategy is based on reinforcement learning. It takes into account which uncertainty has to be minimized at each point in time (for example, relative ball position uncertainty vs. robot location uncertainty).






Robot Localization

Robot localization is an important application driving our research in belief representations and particle filtering for state estimation. Localization is one of the most fundamental problems in mobile robotics. With our collaborators, we introduced grid-based approaches, tree-based representations, and particle filters for robot localization. We were the first to solve the global localization problem, which requires a robot to estimate its position within an environment from scratch, i.e., without knowledge of its start position.







People Tracking

Knowing and predicting the locations of people moving through an environment is a key component of many pro-active service applications, including mobile robots. Depending on the task and the available sensors, we apply joint probabilistic data association filters, Rao-Blackwellised particle filters, and Voronoi-based particle filters to estimate locations of people. Such estimates build he foundations for learning typical motion patterns of people, as used in the activity recognition project.






Ubiquitous computing

The plant care project helps us to investigate how mobile robots can interact with environments that are equipped with networks of sensors. The task of the robot is to water the plants and calibrate the sensors in the environment.








Museum Tour-guide Robots

The reliability of probabilistic methods for mobile robot navigation has been demonstrated during the deployment of the mobile robots Rhino and Minerva as tour-guides in two populated museums. The task of these robots was to guide people through the exhibitions of the ``Deutsches Museum Bonn'', Germany, and the ``National Museum of American History'' in Washington, D.C.