Data-Efficient Robot Learning


On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, machine learning is a promising framework for automatically learning to solve problems. While machine learning has had many successes recently, a practical challenge we face is its data inefficiency: In real-world problems (e.g., robotics) it is not always possible to conduct millions of experiments, e.g., due to time or hardware constraints. In this talk, I will discuss two approaches toward data-efficient robot learning: model-based reinforcement learning and Bayesian optimization.

Key references

  • Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015
  • Roberto Calandra, Jan Peters, André Seyfarth, Marc P. Deisenroth. An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion. Proceedings of the IEEE International Conference on Robotics and Automation, 2014.
  • Date
    AI Core Talks Series
    Marc Deisenroth
    DeepMind Chair in Artificial Intelligence