DARPA Robotics Challenge Trials Research Articles

Team SNU's Control Strategies for Enhancing a Robot's Capability: Lessons from the 2015 DARPA Robotics Challenge Finals

This paper presents the technical approaches used and experimental results obtained by Team SNU (Seoul National University) at the 2015 DARPA Robotics Challenge (DRC) Finals. Team SNU is one of the newly qualified teams, unlike 12 teams who previously participated in the December 2013 DRC Trials. The hardware platform THORMANG, which we used, has been developed by ROBOTIS. THORMANG is one of the smallest robots at the DRC Finals. Based on this platform, we focused on developing software architecture and controllers in order to perform complex tasks in disaster response situations and modifying hardware modules to maximize manipulability. Ensuring stability and modularization are two main keywords in the technical approaches of the architecture. We designed our interface and controllers to achieve a higher robustness level against disaster situations. Moreover, we concentrated on developing our software architecture by integrating a number of modules to eliminate software system complexity and programming errors. With these efforts on the hardware and software, we successfully finished the competition without falling, and we ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the 2015 DRC Finals.

Design of a Momentum-Based Control Framework and Application to the Humanoid Robot Atlas

This paper presents a momentum-based control framework for floating-base robots and its application to the humanoid robot “Atlas”. At the heart of the control framework lies a quadratic program that reconciles motion tasks expressed as constraints on the joint acceleration vector with the limitations due to unilateral ground contact and force-limited grasping. We elaborate on necessary adaptations required to move from simulation to real hardware and present results for walking across rough terrain, basic manipulation, and multi-contact balancing on sloped surfaces (the latter in simulation only). The presented control framework was used to secure second place in both the DARPA Robotics Challenge Trials in December 2013 and the Finals in June 2015.

From Autonomy to Cooperative Traded Control of Humanoid Manipulation Tasks with Unreliable Communication

In this paper, we present our system design, operational procedure, testing process, field results, and lessons learned for the valve-turning task of the DARPA Robotics Challenge (DRC). We present a software framework for cooperative traded control that enables a team of operators to control a remote humanoid robot over an unreliable communication link. Our system, composed of software modules running on-board the robot and on a remote workstation, allows the operators to specify the manipulation task in a straightforward manner. In addition, we have defined an operational procedure for the operators to manage the teleoperation task, designed to improve situation awareness and expedite task completion. Our testing process, consisting of hands-on intensive testing, remote testing, and remote practice runs , demonstrates that our framework is able to perform reliably and is resilient to unreliable network conditions. We analyze our approach, field tests, and experience at the DRC Trials and discuss lessons learned which may be useful for others when designing similar systems.

Team IHMC's Lessons Learned from the DARPA Robotics Challenge Trials

This article is a summary of the experiences of the Florida Institute for Human & Machine Cognition (IHMC) team during the DARPA Robotics Challenge (DRC) Trials. The primary goal of the DRC is to develop robots capable of assisting humans in responding to natural and manmade disasters. The robots are expected to use standard tools and equipment to accomplish the mission. The DRC Trials consisted of eight different challenges that tested robot mobility, manipulation, and control under degraded communications and time constraints. Team IHMC competed using the Atlas humanoid robot made by Boston Dynamics. We competed against 16 international teams and placed second in the competition. This article discusses the challenges we faced in transitioning from simulation to hardware. It also discusses the lessons learned both during the competition and in the months of preparation leading up to it. The lessons address the value of reliable hardware and solid software practices. They also cover effective approaches to bipedal walking and designing for human‐robot teamwork. Lastly, the lessons present a philosophical discussion about choices related to designing robotic systems.

Mobile Manipulation and Mobility as Manipulation—Design and Algorithms of RoboSimian

This article presents the hardware design and software algorithms of RoboSimian, a statically stable quadrupedal robot capable of both dexterous manipulation and versatile mobility in difficult terrain. The robot has generalized limbs and hands capable of mobility and manipulation, along with almost fully hemispherical three‐dimensional sensing with passive stereo cameras. The system is semiautonomous, enabling low‐bandwidth, high latency control operated from a standard laptop. Because limbs are used for mobility and manipulation, a single unified mobile manipulation planner is used to generate autonomous behaviors, including walking, sitting, climbing, grasping, and manipulating. The remote operator interface is optimized to designate, parametrize, sequence, and preview behaviors, which are then executed by the robot. RoboSimian placed fifth in the DARPA Robotics Challenge Trials, demonstrating its ability to perform disaster recovery tasks in degraded human environments.

Human‐in‐the‐loop Control of a Humanoid Robot for Disaster Response: A Report from the DARPA Robotics Challenge Trials

The DARPA Robotics Challenge (DRC) requires teams to integrate mobility, manipulation, and perception to accomplish several disaster‐response tasks. We describe our hardware choices and software architecture, which enable human‐in‐the‐loop control of a 28 degree‐of‐freedom Atlas humanoid robot over a limited bandwidth link. We discuss our methods, results, and lessons learned for the DRC Trials tasks. The effectiveness of our system architecture was demonstrated as the WPI‐CMU DRC Team scored 11 out of a possible 32 points, ranked seventh (out of 16) at the DRC Trials, and was selected as a finalist for the DRC Finals.

Meta‐analysis of Autonomy at the DARPA Robotics Challenge Trials

The DARPA Robotics Challenge trials offer insights into how the robotics community approaches the design of intelligent systems and the role of supervision and simulation. A survey of the teams suggests that the design process may be hampered by the lack of a recognized canon of intelligent design principles and references and by the underrepresentation of artificial intelligence experts on the teams. The teams generally approached supervision and simulation as a fine‐grained execution approval activity rather than as task rehearsal for the entire action sequence.

A General‐purpose System for Teleoperation of the DRC‐HUBO Humanoid Robot

We present a general system with a focus on addressing three events of the 2013 DARPA Robotics Challenge (DRC) trials: debris clearing, door opening, and wall breaking. Our hardware platform is DRC‐HUBO, a redesigned model of the HUBO2+ humanoid robot developed by KAIST and Rainbow, Inc. Our system allowed a trio of operators to coordinate a 32 degree‐of‐freedom robot on a variety of complex mobile manipulation tasks using a single, unified approach. In addition to descriptions of the hardware and software, and results as deployed on the DRC‐HUBO platform, we present some qualitative analysis of lessons learned from this demanding and difficult challenge.

Valkyrie: NASA's First Bipedal Humanoid Robot

In December 2013, 16 teams from around the world gathered at Homestead Speedway near Miami, FL to participate in the DARPA Robotics Challenge (DRC) Trials, an aggressive robotics competition partly inspired by the aftermath of the Fukushima Daiichi reactor incident. While the focus of the DRC Trials is to advance robotics for use in austere and inhospitable environments, the objectives of the DRC are to progress the areas of supervised autonomy and mobile manipulation for everyday robotics. NASA's Johnson Space Center led a team comprised of numerous partners to develop Valkyrie, NASA's first bipedal humanoid robot. Valkyrie is a 44 degree‐of‐freedom, series elastic actuator‐based robot that draws upon over 18 years of humanoid robotics design heritage. Valkyrie's application intent is aimed at not only responding to events like Fukushima, but also advancing human spaceflight endeavors in extraterrestrial planetary settings. This paper presents a brief system overview, detailing Valkyrie's mechatronic subsystems, followed by a summarization of the inverse kinematics‐based walking algorithm employed at the Trials. Next, the software and control architectures are highlighted along with a description of the operator interface tools. Finally, some closing remarks are given about the competition, and a vision of future work is provided.

Team THOR's Entry in the DARPA Robotics Challenge Trials 2013

This paper describes the technical approach, hardware design, and software algorithms that have been used by Team THOR in the DARPA Robotics Challenge (DRC) Trials 2013 competition. To overcome big hurdles such as a short development time and limited budget, we focused on forming modular components—in both hardware and software—to allow for efficient and cost‐effective parallel development. The hardware of THOR‐OP (Tactical Hazardous Operations Robot–Open Platform) consists of standardized, advanced actuators and structural components. These aspects allowed for efficient maintenance, quick reconfiguration, and most importantly, a relatively low build cost. We also pursued modularity in the software, which consisted of a hybrid locomotion engine, a hierarchical arm controller, and a platform‐independent remote operator interface. These modules yielded multiple control options with different levels of autonomy to suit various situations. The flexible software architecture allowed rapid development, quick migration to hardware changes, and multiple parallel control options. These systems were validated at the DRC Trials, where THOR‐OP performed well against other robots and successfully acquired finalist status.

Optimization‐based Full Body Control for the DARPA Robotics Challenge

We describe our full body humanoid control approach developed for the simulation phase of the DARPA Robotics Challenge (DRC), as well as the modifications made for the DARPA Robotics Challenge Trials. We worked with the Boston Dynamics Atlas robot. Our approach was initially targeted at walking, and it consisted of two levels of optimization: a high‐level trajectory optimizer that reasons about center of mass and swing foot trajectories, and a low‐level controller that tracks those trajectories by solving floating base full body inverse dynamics using quadratic programming. This controller is capable of walking on rough terrain, and it also achieves long footsteps, fast walking speeds, and heel‐strike and toe‐off in simulation. During development of these and other whole body tasks on the physical robot, we introduced an additional optimization component in the low‐level controller, namely an inverse kinematics controller. Modeling and torque measurement errors and hardware features of the Atlas robot led us to this three‐part approach, which was applied to three tasks in the DRC Trials in December 2013.

Human‐robot Teaming for Rescue Missions: Team ViGIR's Approach to the 2013 DARPA Robotics Challenge Trials

Team ViGIR entered the 2013 DARPA Robotics Challenge (DRC) with a focus on developing software to enable an operator to guide a humanoid robot through the series of challenge tasks emulating disaster response scenarios. The overarching philosophy was to make our operators full team members and not just mere supervisors. We designed our operator control station (OCS) to allow multiple operators to request and share information as needed to maintain situational awareness under bandwidth constraints, while directing the robot to perform tasks with most planning and control taking place onboard the robot. Given the limited development time, we leveraged a number of open source libraries in both our onboard software and our OCS design; this included significant use of the robot operating system libraries and toolchain. This paper describes the high level approach, including the OCS design and major onboard components, and it presents our DRC Trials results. The paper concludes with a number of lessons learned that are being applied to the final phase of the competition and are useful for related projects as well.

An Architecture for Online Affordance‐based Perception and Whole‐body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field‐realistic communications link. We describe the design considerations, architecture, implementation, and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule.