DARPA Robotics Challenge Track B Team Steel

Atkeson Group, Robotics Institute, Carnegie Mellon University

We are collaborating with WRECS (WPI Robotics Engineering C-track Squad) in the DRC Competition


Please help us support participation by students and others by sending us a donation.

Credit Card

You can use the CMU Donation Page to donate using a credit card (on the 2nd page put "Other" as the Designation. A new box Preferred Designation will appear. Type "Team Steel" in that box.)


You can send a check payable to "CMU Robotics Institute" to:
Chris Atkeson/Team Steel
CMU Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA, USA, 15213

DARPA Robotics Challenge Background Information

DARPA Robotics Challenge Website

There are two DARPA Robotics Challenge groups at CMU: A Track A group (Team Tartan Rescue) led by Tony Stentz that will build a robot, and a Track B group (Team Steel) led by Chris Atkeson that will use a DARPA-provided robot. More information on the Atkeson Team Steel effort is provided below.

If you would like to join Team Steel, send email to Chris Atkeson: cga at cmu.edu

If you would like to join Team Tartan Rescue, send email to Tony Stentz: axs at rec.ri.cmu.edu

Some prior work on ball juggling by Team Steel

Videos, Papers, and Powerpoint

Videos of our work are on the web pages of alumnus Ben Stephens.

Papers from our group are available from Chris Atkeson's web page.

Powerpoint and movies shown at Dynamic Walking 2012 relevant to DRC.

Some prior work on devil stick juggling by Team Steel

Team Steel Approaches


We are using robot learning in several ways.

Optimal Control

We use optimal control and optimization as our major planning tool. An important research question is whether optimization using models will work in the messy real world.

A related research question is whether we can generate robot behavior that is robust. In our previous work, our robots worked great in our lab, but software running on identical robots in other places did not work well. What will it take to write robot programs that work well in our lab, other lab situations, and in the messy real world?

A particular specialty of our group is fast robust policy (control law) optimization using multiple models (alternative universes). This approach generates behavior that works well for a variety of possible robots and worlds.

Be Human-Like

We will attempt to mimic human task strategies, and also the soft (compliant) human touch. Most robots today are very stiff, and it is difficult for the robot to let the task or environment guide movement.


We see this challenge as an opportunity to get students interested in robotics, engineering, and science. We will explore how we can facilitate STEM (Science, Technology, Engineering, and Mathematics) outreach.