The State of Imitation Learning:

Understanding its Applications and Promoting its Adoption

June 27, 2011

Robotics Science and Systems

Los Angeles, California, USA

Imitation learning has grown to be a large field with applications across robotics, neural computation, and artificial intelligence.  As the field has developed, ideas have sprouted from a wide range of motivations and applications  resulting differing terminology and significant overlap; terms such as apprenticeship learning, learning from demonstration, inverse optimal control, and inverse reinforcement learning  mean the same thing to some, while to others they have vastly different connotations.  Imitation learning is already creating a stir within the robotics community as an effective and practical way to transfer our intuition to real world robotics systems, and has the potential to revolutionize the way we approach system development.  In this workshop, we will examine the collection of subfields within imitation learning together and attempt to construct a formal taxonomy of the tools and techniques available to solidify its foundation and promote wider adoption with the robotics community.

This full­day workshop will study the numerous subareas of imitation learning in order to synthesize and summarize the lessons learned and to draw connections between the array of tools available. The structure will consist primarily of invited speakers who are leaders within the field, as well as a poster session of committee ­reviewed extended abstract submissions.


We welcome submissions of 2­-3 page extended abstracts for participation in the poster session. Submissions that highlight recent work and results in applying imitation learning to real robotics problems   are   encouraged   in   particular.   Top   submissions   may   be   invited   to   give   a   short  oral presentation  during  the workshop. 

Submissions should be emailed to

Important Dates 

Submission deadline:        

May 9 (Extended)

Acceptance notification:    

May 27  


June 27



Brenna Argall, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland

Nathan Ratliff, Google, Pittsburgh, PA

David Silver, Carnegie Mellon University, Pittsburgh, PA

Program Committee

S.M. Khansari-Zadeh, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland

Zico Kolter, Massachusetts Institute of Technology, Cambridge, MA

Stephane Ross, Carnegie Mellon University, Pittsburgh, PA

Matt Zucker, Swarthmore College, Philadelphia, PA

Confirmed Speakers

Pieter Abbeel, University of California, Berkeley, CA

Drew Bagnell, Carnegie Mellon University, Pittsburgh, PA

Aude Billard, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland

Chad Jenkins, Brown University, Providence, RI

Jan Peters, Max Planck Institute for Intelligent Systems / Darmstadt University of Technology


09:00 [40m]         Introduction

09:40 [40m]        Chad Jenkins

10:20 [10m]        Submission: Teaching Robots to Execute Verb Phrases

10:30 [15m]           coffee

10:45 [40m]         Organizers

11:25 [40m]         Pieter Abbeel

12:05 [10m]        Submission: Toward Imitating Object Manipulation Tasks

Using Sequences of Movement Dependency Graphs

12:15 [1h15m]           lunch

13:30 [40m]         Drew Bagnell

14:10 [40m]         Aude Billard

14:50 [10m]         Submission: Blending Autonomous and Apprenticeship Learning

15:00 [30m]           coffee

15:30 [40m]         Jan Peters

16:10 [50m]         Open Discussion

17:00                   Workshop Ends

Invited Talks

Chad Jenkins: rosbridge: ROS for non-ROS users

Reproducibility, interoperability, and accessibility are critical needs for a thriving ecosystem of robotics research and development. These needs are especially true for robot learning from demonstration, where interactions with actual human users is essential.  The combination of off-the-shelf robotics platforms, cloud computing, and common interchange protocols has the power to enable robots to be used like any other web services, opening vast new applications for robotics and robot learning.  In this talk, I cover our recent work with rosbridge as a bridge to a Robot Operating System (ROS) that is independent of any specific operating system or build environment. Treating the ROS run-time environment as a robot server (similar to Apache for web content), I describe our robot web applications implemented purely through JavaScript/HTML for web-scale robot learning and a PR2 Remote Lab.  Such applications demonstrate "no-install" interfaces for reaching broader populations of users as well as platforms for common decentralized experimentation.

Pieter Abbeel: Apprenticeship Learning for Autonomous Flight and Surgical Robotics

For many problems in robotics performance under tele-operation is far higher than under autonomous operation. In this talk I will present apprenticeship learning algorithms, which enable experts to teach robots through demonstrations. Our apprenticeship learning techniques have enabled a helicopter to perform advanced aggressive maneuvers, well beyond the prior state of the art, including maneuvers such as chaos and tic-tocs, which only exceptional expert human pilots can fly. I will also describe our preliminary results towards automating selected surgical skills.

Drew Bagnell: Computational Rationalization

I'll review the roles inverse optimal control can play in real-world robotics. I'll further discuss my personal view of the frontiers of such methods, including multiple agents and transitioning learning surrogate cost functions from imitation learning to reinforcement learning.

Aude Billard: Overview of 15 years of research in imitation learning

I will review the various works we did over the past 15 years: starting from the robot Robota and its use with autistic children, revising the various computational models of human imitation we developped and highlighting how these models informed our current robotics work. I will conclude with a few pointers on topics which I view of particular interest in the field. These include how to combine imitation learning with other learning techniques, how to learn from failed demonstrations and how to bridge the gap from trajectory level imitation to behavior-based imitation.

Jan Peters

Submitted Contributions

Teaching Robots to Execute Verb Phrases, Daniel Hewlett, Thomas J. Walsh, Paul Cohen [pdf]

Toward Imitating Object Manipulation Tasks Using Sequences of Movement Dependency Graphs, Vladimir Sukhoy, Shane Griffith, and Alexander Stoytchev [pdf]

Blending Autonomous and Apprenticeship Learning,Thomas J. Walsh and Daniel Hewlett and Clayton T. Morrison [pdf]