The DARPA Robotics Challenge (DRC) program conducted a series of prize‐based competition events to develop and demonstrate technology for disaster response. This article provides the official and definitive account of DRC Finals as the culmination of the DRC program. The article details the eight tasks (Drive, Egress, Door, Valve, Wall, Surprise [Plug and Switch], Rubble [Obstacle or Debris], and Stairs) constituting the Challenge, and describes how the competition encouraged supervised autonomous operation by intentionally degrading the communications channel between the remote human operators. The article presents the results of the DRC Finals and places those results in perspective by identifying both strengths and weaknesses of robot performance exhibited at the competition.

This paper summarizes an ongoing research program to advance the state of the art in robotics: the U.S. Defense Advanced Research Projects Agency Autonomous Robotic Manipulation (ARM) program. The program began in 2010 with three tracks, which was later extended to four. The software track is developing intelligent control of manipulators to perform autonomous tasks, using local perception sensors. The hand track has developed rugged, dexterous, multi-fingered hands with significantly reduced costs. The arm track is working to reduce the cost of robot arms. And the outreach track is showcasing robot technology to the general public. Technology developed under the program is iteratively evaluated through a series of hands-off tests. To date, the ARM developers have performed beyond expectations, yielding outstanding hardware designs and robust manipulation software. The results of this program are expected to strengthen the general robotics community and transition technology to U.S. military efforts.

This paper summarizes an ongoing research program to advance the state of the art in robotics: the U.S. Defense Advanced Research Projects Agency Autonomous Robotic Manipulation (ARM) program. The program began in 2010 with three tracks, which was later extended to four. The software track is developing intelligent control of manipulators to perform autonomous tasks, using local perception sensors. The hand track has developed rugged, dexterous, multi-fingered hands with significantly reduced costs. The arm track is working to reduce the cost of robot arms. And the outreach track is showcasing robot technology to the general public. Technology developed under the program is iteratively evaluated through a series of hands-off tests. To date, the ARM developers have performed beyond expectations, yielding outstanding hardware designs and robust manipulation software. The results of this program are expected to strengthen the general robotics community and transition technology to U.S. military efforts. D. Hackett (B) Griffin Technologies, Fort Washington, PA, USA e-mail: dhackett@griffin-technologies.com J. Pippine Golden Knight Technologies, Fairfax, VA, USA e-mail: Jim@goldenknighttechnologies.com A. Watson · C. Sullivan Mechanismo, Reston, VA, USA e-mail: adam@mechanismollc.com C. Sullivan e-mail: chad@mechanismollc.com G. Pratt Defense Advanced Research Projects Agency (DARPA), Arlington, VA, USA e-mail: gpratt@darpa.mil

Circa 2010, manipulation systems required burdensome human operation and high-precision arms and hands, yet could not match the manipulation skills of a two-year old child.To address this situation, the U.S.

views, opinions, and/or findings contained in this article/presentation are those of the author/ presenter and should not be interpreted as represent-ing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.

In November of 2005, the Defense Advanced Research Projects Agency initiated a new robotics program, Learning Locomotion, designed to solve some of the key outstanding issues. The expectation was that a combination of machine learning techniques and smart development would accelerate the pace of making autonomous legged systems robust and useful.

Research teams use common robots and machine learning to teach the robots outdoor navigation and locomotion skills.

A long-held dream for robotics researchers is the creation of vehicles that can move to a goal without human supervision, adapting as required to changing circumstances. While today’s ground robots are still far from achieving such complete autonomy, substantial progress has been attained. In this paper we describe the state-of-the-art in autonomous ground vehicle navigation as observed in the recently completed DARPA PerceptOR program, and we suggest new research directions where we see opportunities for leaps in performance.