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.

The U.S. Defense Advanced Research Projects Agency's (DARPA) Neovision2 program aims to develop artificial vision systems based on the design principles employed by mammalian vision systems. Three such algorithms are briefly described in this paper. These neuromorphic-vision systems' performance in detecting objects in video was measured using a set of annotated clips. This paper describes the results of these evaluations including the data domains, metrics, methodologies, performance over a range of operating points and a comparison with computer vision based baseline algorithms.

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.

The principal goal of the LANdroids program (2007-2010) was to validate the concept that mobile tactical radio relay platforms can provide improved communications connectivity in non-line-of-sight communications environments such as urban terrain. The first phase of the program demonstrated that intelligent mobile relays can provide improved system performance in network configuration, optimization, and self-healing, and the second phase added additional capabilities including intruder detection and situational awareness, and included a real-world demonstration to potential users.

Abstract : The purpose of this project was to develop methods and software to determine whether a given Small Unmanned Ground Vehicle (SUGV) can traverse a given terrain, when both the SUGV and the terrain are not known exactly. A simulation model of a real-world SUGV (iRobot PackBot) was developed in the ADAMS environment and used to simulate traversal of a variable-height step obstacle. For this project, a user subroutine was successfully integrated into the ADAMS model to predict deformable track-terrain interaction. A parameterizable UGV vehicle system model was implemented using the ADAMS command language. A key element of this model is a slip-sinkage model. This simulation model was validated using real-world data collected in a step validation fixture with sand and a variable-height curb. In a related effort, this project developed methods for using UGV sensor data to estimate variables and parameters needed for traction force prediction. The methods were evaluated using data collected from experiments with a PackBot traversing various deformable and non-deformable surfaces.

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.