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Welcome to my homepage at the Intelligent Autonomous Systems Group at the Technische Universität München. I did my PhD on "Tailoring Robot Actions to Task Contexts using Action Models" here from 2002-2007, and was a postdoctoral researcher in the CogMan project ("Cognitive Models of Everyday Manipulation Tasks") in 2008/2009. I am currently at the Computational Learning and Motor Control Lab at the University of Southern California, where you can find my most up-to-date homepage.

Research Topics

  • Motion Primitives
  • Human and Robot Manipulation
  • Human Motions as Model for Robot Controllers
  • Plan Optimization
  • Human Motion Capture, Tracking and Analysis
  • Human Motions in Manipulation Tasks
  • Objective Functions, Learning Objective Functions

Current Research Projects

  • CogMan : The CogMan project (1) develops computational and control models of pick-and-place tasks in the context of everyday manipulation activities in human environments, (2) implements the model into a control system for the kitchen scenario, and (3) empirically analyzes the impact of this control model on the flexibility, robustness, adaptability, and naturality of the robot behavior.
  • Face Image Analysis : Recent progress in the field of Computer Vision allows intuitive interaction via gesture or facial expressions between humans and technical systems. This project aims at determining facial expressions from camera images in real-time. Model-based image interpretation techniques have proven to be a successful method for extracting such high-level information from single images and image sequences. Models rely on a priori knowledge to reduce the large amount of image data to a small number of model parameters. This representation of the image content facilitates and accelerates the subsequent interpretation task. In order to extract high-level information, model parameters have to be estimated that best describe the face within a given image. However, correctly estimated model parameters forms the basis of various more applications such as gaze detection or gender estimation. Our demonstrator for facial expression estimation has been presented at several events with political audience and on TV.

Former Research Projects

  • Agilo : Robotic soccer has become a standard 'real-world' testbed for autonomous multi robot control. In robot soccer (mid-size league) two teams of four autonomous robots --- one goal keeper and three field players --- play soccer against each other. The soccer field is four by nine meters big. The key characteristics of mid-size robot soccer are that the robots are completely autonomous. Consequently, all sensing and all action selection is done onboard of the individual robots. Skillful play requires our robots to recognize objects, such as other robots, field lines, and goals, and even entire game situations. In the AGILO project we investigate how probabilistic visuomotoric autonomous robot controllers that are capable of learning can meet these challenges. The AGILO robot controllers employ game state estimation and situated action selection based on automatically learned control mechanisms.

Selected Publications

  • Freek Stulp , Michael Beetz , Refining the execution of abstract actions with learned action models, 2008, Journal of Artificial Intelligence Research (JAIR),
    (BibTeX)
  • Freek Stulp , Michael Beetz , Combining Declarative, Procedural and Predictive Knowledge to Generate and Execute Robot Plans Efficiently and Robustly, 2008, Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge),
    (BibTeX)

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