Carnegie Mellon University Robotics Institute Homepage

Carnegie Mellon University Robotics Institute Research Guide

Carnegie Mellon University, Robotics Institute, Research Guide

Machine Learning

Machine learning (ML) permeates the Institute and plays an integral role in many robotics sub-fields as well as in many on-going projects. Several of our faculty (Atkeson, Bagnell, Dubrawski, Gordon, Guestrin and Schneider) specialize in the foundations of learning. Each focuses on questions about learning within autonomous control and sensing. These horizontal technologies are used throughout the institute and include:

We review below just a few of the areas within Robotics for which machine learning has become a core technology:

Autonomous science

Much of the recent effort within the Institute can be categorized as Assisted or Autonomous Science where robots serve as helpers and co-discoverers of knowledge. This includes the development of active learning methods that attempt to efficiently find cosmological parameters of the universe via testing observational and simulated data (Schneider). RI (Schneider) has successfully commercialized a system that automatically recognizes psycho-active drugs by their effects on mice and is able to autonomously run experiments to identify substances with potential therapeutic effect. Learning approaches to modeling spatial phenomena have been applied to algal bloom detection and monitoring (Dolan, Guestrin) and autonomous robotic exploration of geologic properties to guide robots (Wettergreen) toward informative areas for exploration. Field experiments demonstrate that these systems are able to successfully operate on multi square-kilometer scales.

Multi-agent Learning

Machine learning techniques are playing an increasing role in the analysis and synthesis of multi-robot and multi-agent behavior. Learning methods have been developed (Gordon, Sycara) that enable multi-agents to adapt to the shifting landscape of other agents and collectively achieve equilibrium strategies. No-regret learning tools have been developed as a mechanism for multi-agent planning (Gordon), and close relationships have been explored with learning in market-based distributed control systems. (Dias, Stentz).

Outlook

Learning is part of the research of a large and growing share of the Robotics faculty. Dozens of graduate students include machine learning in their thesis research. The dissemination throughout the Institute of research in machine learning has led to many collaborations:

In recent years, sponsors and partners have become increasingly savvy about the potential of machine learning technology and have funded a variety of programs that either explicitly or implicitly call for research in this area. Trends suggest an increase in these efforts and some sponsors (e.g. DARPA, ARL) have indicated machine learning is a long-term priority. Machine Learning within RI is tightly integrated with machine learning research activities through SCS and Carnegie Mellon. For instance, many of our faculty (e.g. Atkeson, Bagnell, Mostow, Schneider, Dubrawski, Gordon, Guestrin, Sycara) are affiliated with SCS's Machine Learning Department (MLD).

Given the unique challenges in robotics and the ubiquity of learning-based solutions to robotics problems, the future prospects for machine learning with the Institute seems assured. There remains, however, an important question of how the Robotics Institute should position itself with respect to sibling organizations within Carnegie Mellon. Two paths immediately present themselves:

In the same way that the Institute actively recruits faculty, staff and students with expertise in electrical and mechanical engineering and computer science, RI can treat learning as a core competency and a priority to grow and develop. While we do not plan to specialize in all areas of machine learning, specific research areas exist for which the Institute is particularly well-suited. For example, we are poised to become world leaders in the study of learning for decision-making and control as well as learning in real-time systems. This may be further encouraged by faculty and special faculty hires in these areas.

At the same time we can greatly benefit from interaction with our institutional siblings; it is likely, for example, that an increasing number of Robotics faculty will become affiliated with the ML department. Additionally, the tremendous scale of data developed within the field of robotics will provide natural opportunities for collaboration with faculty interested in large scale and distributed computation.

Education

The institute currently offers a variety of graduate classes that focus explicitly on machine learning. Statistical Techniques in Robotics provides a foundation in both Bayesian probabilistic modeling techniques as well on developing and using online learning algorithms for robotic applications. Learning-based Methods in Vision, explores how data driven techniques and machine learning can enhance machine understanding of the visual world. Adaptive Control and Reinforcement Learning focuses on the role Machine Learning plays in the making decisions and developing strategies and controllers. Additionally, RI faculty regularly teach core classes within the Machine Learning department.

Continue Reading: Manipulation & Control


Faculty

  1. Omead
    Amidi

  2. Christopher
    Atkeson

  3. Drew
    Bagnell

  4. John
    Bares

  5. Fernando
    De la Torre

  6. Bernardine
    Dias

  7. John
    Dolan

  8. Artur
    Dubrawski

  9. Alexei
    Efros

  10. Geoffrey
    Gordon

  11. Carlos
    Guestrin

  12. Martial
    Hebert

  13. Takeo
    Kanade

  14. Alonzo
    Kelly

  15. Jack
    Mostow

  16. Nancy
    Pollard

  17. Jeff
    Schneider

  18. Reid
    Simmons

  19. Sanjiv
    Singh

  20. Aaron
    Steinfeld

  21. Tony
    Stentz

  22. Katia
    Sycara

  23. Manuela
    Veloso

  24. David
    Wettergreen


Project Images

  • Learning Rough Terrain Navigation

  • Deep Inference Machines

  • River Detection

  • Safeguarding

  • Active learning in Cosmology and automated drug pyschiatric drug discovery

  • Intelligent Tutoring Systems/ Project LISTEN

  • Optimized Environmental Sensing

  • Visual Memex

  • Autonomous Science

  • Data Aggregation

  • RVCA Photos

  • Inverse Optimal Control for Imigation Learning

  • AHS: Learning in Fielded and Commercial Systems