About

Our research focuses on skill learning in robots and learning agents in general, and the development of intelligent systems that can act autonomously in uncertain and unstructured environments. Towards this goal, we study different perceptual-motor learning tasks on various robotic platforms (humanoids, manipulators, and mobile robots) in simulations and real environments and use methods from robotics and machine learning, in particular, imitation, reinforcement, and deep learning. We combine data-driven approaches with model-based formulations by exploring the mathematical structure of intelligent systems acting in a complex environment in terms of geometry, optimal control, and probability theory. Since human capabilities are far beyond those of artificial systems, we try to gain further insights into intelligent systems from studies in human motor control, biomimetics, neuroscience, and psychology.

Current Research Topics:

  • Learning agents and intelligent robots
  • Reinforcement learning
  • Imitation learning
  • Mathematics for intelligent systems

NEWS AND HIGHLIGHTS:

DFG grant (2021-2024) jointly awarded with Jan Peters, Technical University of Darmstadt, Germany

Evaluating Guided Policy Search for human handovers

Alap Kshirsagar, Guy Hoffman, Armin Biess – ICRA 2021/R-AL

Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization

Binyamin Manela, Armin Biess – Neurocomputing

Siemens Challenge: Learning a high-precision robotic assembly task using pose estimation from simulated depth images

Yuval Litvak, Armin Biess and Aharon Bar-Hillel