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The DARPA Robotics Challenge was a robotics competition that took place in Pomona, California USA in June 2015. The competition was the culmination of 33 months of demanding work by 23 teams and required humanoid robots to perform challenging locomotion and manipulation tasks in a mock disaster site. The challenge was conceived as a response to the Japanese Fukushima nuclear disaster of March 2011. The Fukushima disaster was seen as an ideal candidate for robotic intervention since the risk of exposure to radiation prevented human responders from accessing the site.

This volume, edited by Matthew Spenko, Stephen Buerger, and Karl Iagnemma, includes commentary by the organizers, overall analysis of the results, and documentation of the technical efforts of 15 competing teams. The book provides an important record of the successes and failures involved in the DARPA Robotics Challenge and provides guidance for future needs to be addressed by policy makers, funding agencies, and the robotics research community.

Many of the papers in this volume were initially published in a series of special issues of the Journal of Field Robotics. We have proudly collected versions of those papers in this STAR volume.



The DARPA Robotics Challenge Finals: Results and Perspectives

The DARPA Robotics Challenge (DRC) program conducted a series of prize-based competition events to develop and demonstrate technology for disaster response. This chapter provides the official and definitive account of DRC Finals as the culmination of the DRC program. The chapter 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 chapter 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.
Eric Krotkov, Douglas Hackett, Larry Jackel, Michael Perschbacher, James Pippine, Jesse Strauss, Gill Pratt, Christopher Orlowski

Robot System of DRC-HUBO+ and Control Strategy of Team KAIST in DARPA Robotics Challenge Finals

This paper summarizes how Team KAIST prepared for the DARPA Robotics Challenge (DRC) Finals, especially in terms of the robot system and control strategy. To imitate the Fukushima nuclear disaster situation, the DRC performed a total of eight tasks and degraded communication conditions. This competition demanded various robotic technologies such as manipulation, mobility, telemetry, autonomy, localization, etc. Their systematic integration and the overall system robustness were also important issues in completing the challenge. In this sense, this paper presents a hardware and software system for the DRC-HUBO+, a humanoid robot that was used for the DRC; it also presents control methods such as inverse kinematics, compliance control, a walking algorithm, and a vision algorithm, all of which were implemented to accomplish the tasks. The strategies and operations for each task are briefly explained with vision algorithms. This paper summarizes what we learned from the DRC before the conclusion. In the competition, 25 international teams participated with their various robot platforms. We competed in this challenge using the DRC-HUBO+ and won first place in the competition.
Jeongsoo Lim, Hyoin Bae, Jaesung Oh, Inho Lee, Inwook Shim, Hyobin Jung, Hyun Min Joe, Okkee Sim, Taejin Jung, Seunghak Shin, Kyungdon Joo, Mingeuk Kim, Kangkyu Lee, Yunsu Bok, Dong-Geol Choi, Buyoun Cho, Sungwoo Kim, Jungwoo Heo, Inhyeok Kim, Jungho Lee, In So Kwon, Jun-Ho Oh

Team IHMC’s Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble

This article presents a retrospective analysis of Team IHMC’s experience throughout the DARPA Robotics Challenge (DRC), where we took first or second place overall in each of the three phases. As an extremely demanding challenge typical of DARPA, the DRC required rapid research and development to push the boundaries of robotics and set a new benchmark for complex robotic behavior. We present how we addressed each of the eight tasks of the DRC and review our performance in the Finals. While the ambitious competition schedule limited extensive experimentation, we will review the data we collected during the approximately three years of our participation. We discuss some of the significant lessons learned that contributed to our success in the DRC. These include hardware lessons, software lessons, and human-robot integration lessons. We describe refinements to the Coactive Design methodology that helped our designers connect human-machine interaction theory to both implementation and empirical data. This approach helped our team focus our limited resources on the issues most critical to success. In addition to helping readers understand our experiences in developing on a Boston Dynamics Atlas robot for the DRC, we hope this article will provide insights that apply more widely to robotics development and design of human-machine systems.
Matthew Johnson, Brandon Shrewsbury, Sylvain Bertrand, Duncan Calvert, Tingfan Wu, Daniel Duran, Douglas Stephen, Nathan Mertins, John Carff, William Rifenburgh, Jesper Smith, Chris Schmidt-Wetekam, Davide Faconti, Alex Graber-Tilton, Nicolas Eyssette, Tobias Meier, Igor Kalkov, Travis Craig, Nick Payton, Stephen McCrory, Georg Wiedebach, Brooke Layton, Peter Neuhaus, Jerry Pratt

Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge

CHIMP, the CMU Highly Intelligent Mobile Platform, is a humanoid robot capable of executing complex tasks in dangerous, degraded, human-engineered environments, such as those found in disaster response scenarios. CHIMP is uniquely designed for mobile manipulation in challenging environments, as the robot performs manipulation tasks using an upright posture, yet uses more stable prostrate postures for mobility through difficult terrain. In this paper, we report on the improvements made to CHIMP—both in its mechanical design and its software systems—in preparation for the DARPA Robotics Challenge Finals in June 2015. These include details on CHIMP’s novel mechanical design, actuation systems, robust construction, all terrain mobility, supervised autonomy approach, and unique user interfaces utilized for the challenge. Additionally, we provide an overview of CHIMP’s performance and detail the various lessons learned over the course of the challenge. CHIMP was one of the winners of the DARPA Robotics Challenge, completing all tasks and finishing 3rd place of 23 teams. Notably, CHIMP was the only robot to stand back up after accidentally falling over, a testament to the robustness engineered into the robot and a remote operator’s ability to execute complex tasks using a highly capable robot. We present CHIMP as a concrete engineering example of a successful disaster response robot.
G. Clark Haynes, David Stager, Anthony Stentz, J Michael Vande Weghe, Brian Zajac, Herman Herman, Alonzo Kelly, Eric Meyhofer, Dean Anderson, Dane Bennington, Jordan Brindza, David Butterworth, Chris Dellin, Michael George, Jose Gonzalez-Mora, Morgan Jones, Prathamesh Kini, Michel Laverne, Nick Letwin, Eric Perko, Chris Pinkston, David Rice, Justin Scheifflee, Kyle Strabala, Mark Waldbaum, Randy Warner

DRC Team NimbRo Rescue: Perception and Control for Centaur-Like Mobile Manipulation Robot Momaro

Robots that solve complex tasks in environments too dangerous for humans to enter are desperately needed, e.g. for search and rescue applications. We describe our mobile manipulation robot Momaro, with which we participated successfully in the DARPA Robotics Challenge. It features a unique locomotion design with four legs ending in steerable wheels, which allows it both to drive omnidirectionally and to step over obstacles or climb. Furthermore, we present advanced communication and teleoperation approaches, which include immersive 3D visualization, and 6D tracking of operator head and arm motions. The proposed system is evaluated in the DARPA Robotics Challenge, the DLR SpaceBot Camp 2015, and lab experiments. We also discuss the lessons learned from the competitions and present initial steps towards autonomous operator assistance functions.
Max Schwarz, Marius Beul, David Droeschel, Tobias Klamt, Christian Lenz, Dmytro Pavlichenko, Tobias Rodehutskors, Michael Schreiber, Nikita Araslanov, Ivan Ivanov, Jan Razlaw, Sebastian Schüller, David Schwarz, Angeliki Topalidou-Kyniazopoulou, Sven Behnke

Team RoboSimian: Semi-autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals

This article discusses hardware and software improvements to the RoboSimian system leading up to and during the 2015 DARPA Robotics Challenge (DRC) Finals. Team RoboSimian achieved a 5th place finish by achieving 7 points in 47:59 min. We present an architecture that was structured to be adaptable at the lowest level and repeatable at the highest level. The low-level adaptability was achieved by leveraging tactile measurements from force torque sensors in the wrist coupled with whole body motion primitives. We use the term “behaviors” to conceptualize this low-level adaptability. Each behavior is a contact-triggered state machine that enables execution of short order manipulation and mobility tasks autonomously. At a high level, we focused on a teach-and-repeat style of development by storing executed behaviors and navigation poses in object/task frame for recall later. This enabled us to perform tasks with high repeatability on competition day while being robust to task differences from practice to execution.
Sisir Karumanchi, Kyle Edelberg, Ian Baldwin, Jeremy Nash, Brian Satzinger, Jason Reid, Charles Bergh, Chelsea Lau, John Leichty, Kalind Carpenter, Matthew Shekels, Matthew Gildner, David Newill-Smith, Jason Carlton, John Koehler, Tatyana Dobreva, Matthew Frost, Paul Hebert, James Borders, Jeremy Ma, Bertrand Douillard, Krishna Shankar, Katie Byl, Joel Burdick, Paul Backes, Brett Kennedy

Director: A User Interface Designed for Robot Operation with Shared Autonomy

Operating a high degree of freedom mobile manipulator, such as a humanoid, in a field scenario requires constant situational awareness, capable perception modules, and effective mechanisms for interactive motion planning and control. A well-designed operator interface presents the operator with enough context to quickly carry out a mission and the flexibility to handle unforeseen operating scenarios robustly. By contrast, an unintuitive user interface can increase the risk of catastrophic operator error by overwhelming the user with unnecessary information. With these principles in mind, we present the philosophy and design decisions behind Director—the open-source user interface developed by Team MIT to pilot the Atlas robot in the DARPA Robotics Challenge (DRC). At the heart of Director is an integrated task execution system that specifies sequences of actions needed to achieve a substantive task, such as drilling a wall or climbing a staircase. These task sequences, developed a priori, make online queries to automated perception and planning algorithms with outputs that can be reviewed by the operator and executed by our whole-body controller. Our use of Director at the DRC resulted in efficient high-level task operation while being fully competitive with approaches focusing on teleoperation by highly-trained operators. We discuss the primary interface elements that comprise the Director and provide analysis of its successful use at the DRC.
Pat Marion, Maurice Fallon, Robin Deits, Andrés Valenzuela, Claudia Pérez D’Arpino, Greg Izatt, Lucas Manuelli, Matt Antone, Hongkai Dai, Twan Koolen, John Carter, Scott Kuindersma, Russ Tedrake

Achieving Reliable Humanoid Robot Operations in the DARPA Robotics Challenge: Team WPI-CMU’s Approach

The DARPA Robotics Challenge (DRC) required participating human-robot teams to integrate mobility, manipulation, perception and operator interfaces to complete a simulated disaster mission. We describe our approach to the development of manipulation and locomotion capabilities for the humanoid robot atlas unplugged developed by Boston Dynamics. We focus on our approach, results and lessons learned from the DRC Finals to demonstrate our strategy including extensive operator practice, explicit monitoring for robot errors, adding additional sensing, and enabling the operator to control and monitor the robot at varying degrees of abstraction. Our safety-first strategy worked: we avoided falling and remote operators could safely recover from difficult situations. We were the only team in the DRC Finals that attempted all tasks, scored points (14/16), did not require physical human intervention (a reset), and did not fall in the two missions during the two days of tests. We also had the most consistent pair of runs. We ranked 3rd out of 23 teams when the scores from two official runs were averaged.
Christopher G. Atkeson, P. W. Babu Benzun, Nandan Banerjee, Dmitry Berenson, Christoper P. Bove, Xiongyi Cui, Mathew DeDonato, Ruixiang Du, Siyuan Feng, Perry Franklin, Michael A. Gennert, Joshua P. Graff, Peng He, Aaron Jaeger, Joohyung Kim, Kevin Knoedler, Lening Li, Chenggang Liu, Xianchao Long, Felipe Polido, X. Xinjilefu, Taşkın Padır

Team DRC-Hubo@UNLV in 2015 DARPA Robotics Challenge Finals

This chapter presents a technical overview of Team DRC-Hubo@UNLVs approach to the 2015 DARPA Robotics Challenge Finals (DRC-Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC-Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel-driven navigation, zero-moment point (ZMP)-based locomotion, and position-based manipulation and controls, are described in this chapter. By introducing strategically critical elements and key lessons learned from DRC-Trials 2013 and the testbed of Charleston, we also illustrate how DRC-Hubo has evolved successfully toward the DRC-Finals.
Paul Oh, Kiwon Sohn, Giho Jang, Youngbum Jun, Donghyun Ahn, Juseong Shin, Baek-Kyu Cho

Team SNU’s Control Strategies to Enhancing Robot’s Capability: Lessons from the DARPA Robotics Challenge Finals 2015

This paper presents the technical approaches used and experimental results obtained by Team SNU at the DARPA Robotics Challenge (DRC) Finals 2015. Team SNU is one of the newly qualified teams, unlike the 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 have successfully finished the competition without falling and ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the DRC Finals 2015.
Sanghyun Kim, Mingon Kim, Jimin Lee, Soonwook Hwang, Joonbo Chae, Beomyeong Park, Hyunbum Cho, Jaehoon Sim, Jaesug Jung, Hosang Lee, Seho Shin, Minsung Kim, Joonwoo Ahn, Wonje Choi, Yisoo Lee, Sumin Park, Jiyong Oh, Yongjin Lee, Sangkuk Lee, Myunggi Lee, Sangyup Yi, Kyong-Sok K. C. Chang, Nojun Kwak, Jaeheung Park

Team THOR’s Entry in the DARPA Robotics Challenge Finals 2015

This paper describes Team THOR’s approach to human-in-the-loop disaster response robotics for the 2015 DARPA Robotics Challenge (DRC) Finals. Under the duress of unpredictable networking and terrain, fluid operator interactions and dynamic disturbance rejection become major concerns for effective teleoperation. We present a humanoid robot designed to effectively traverse a disaster environment while allowing for a wide range of manipulation abilities. To complement the robot hardware, a hierarchical software foundation implements network strategies that provide real-time feedback to an operator under restricted bandwidth using layered user interfaces. Our strategy for humanoid locomotion includes a backward facing knee configuration paired with specialized toe and heel lifting strategies that allow the robot to traverse difficult surfaces while rejecting external perturbations. With an upper body planner that encodes operator preferences, predictable motion plans are executed in unforeseen circumstances that are critical for manipulation in unknown environments. Our approach was validated during the DRC Finals competition, where Team THOR scored three points in 18 min of operation time, and the results are presented with analysis of each task.
Stephen G. McGill, Seung-Joon Yi, Hak Yi, Min Sung Ahn, Sanghyun Cho, Kevin Liu, Daniel Sun, Bhoram Lee, Heejin Jeong, Jinwook Huh, Dennis Hong, Daniel D. Lee

Collaborative Autonomy Between High-Level Behaviors and Human Operators for Control of Complex Tasks with Different Humanoid Robots

This chapter discusses the common reactive high-level behavioral control system used by Team ViGIR and Team Hector on separate robots in the 2015 DARPA Robotics Challenge (DRC) Finals. We present an approach that allows one or more human operators to share control authority with a high-level behavior controller in the form of a finite state machine (automaton). This collaborative autonomy leverages the relative strengths of the robotic system and the (remote) human operators; it increases reliability of the human-robot team performance and decreases the task completion time. This approach is well-suited to disaster scenarios due to the unstructured nature of the environment. The system allows the operators to adjust the robotic system’s autonomy on-the-fly in response to changing circumstances, and to modify pre-defined behaviors as needed. To enable these high-level behaviors, we introduce our system designs for several of the lower-level system capabilities such as footstep planning and template-based object manipulation. We evaluate the proposed approach in the context of our two teams’ participation in the DRC Finals using two different humanoid platforms, and in systematic experiments conducted in the lab afterward. We present a discussion about the lessons learned during the DRC, especially those related to transitioning between operator-centered control and behavior-centered control during competition. Finally, we describe ongoing research beyond the DRC that extends the systems developed during the DRC. All of our described software is available as open source software.
David C. Conner, Stefan Kohlbrecher, Philipp Schillinger, Alberto Romay, Alexander Stumpf, Spyros Maniatopoulos, Hadas Kress-Gazit, Oskar von Stryk

WALK-MAN Humanoid Platform

In this chapter we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environments and demonstrate new skills including powerful manipulation, robust balanced locomotion, high strength capabilities and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of Series Elastic Actuation (SEA) drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance benefits from both active and passive adaptation thanks to WALK-MAN actuation, which combines customized high performance modules with tuned torque/velocity curves and transmission elasticity for high speed adaptation response and motion reactions to disturbances. The WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with respect to the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing and control. WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready 4 months before the DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviours synthesized by combining different primitives defining the behaviour of the center of gravity, of the hands, legs and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion generation framework including the specific motion modules and software architecture are discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of tele-operated or semi-autonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DARPA Robotics Challenge in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.
N. G. Tsagarakis, F. Negrello, M. Garabini, W. Choi, L. Baccelliere, V. G. Loc, J. Noorden, M. Catalano, M. Ferrati, L. Muratore, P. Kryczka, E. Mingo Hoffman, A. Settimi, A. Rocchi, A. Margan, S. Cordasco, D. Kanoulas, A. Cardellino, L. Natale, H. Dallali, J. Malzahn, N. Kashiri, V. Varricchio, L. Pallottino, C. Pavan, J. Lee, A. Ajoudani, D. G. Caldwell, A. Bicchi

An Architecture for Human-Guided Autonomy: Team TROOPER at the DARPA Robotics Challenge Finals

Recent robotics efforts have automated simple, repetitive tasks to increase execution speed and lessen an operator’s cognitive load, allowing them to focus on higher-level objectives. However, an autonomous system will eventually encounter something unexpected, and if this exceeds the tolerance of automated solutions there must be a way to fall back to teleoperation. Our solution is a largely autonomous system with the ability to determine when it is necessary to ask a human operator for guidance. We call this approach human-guided autonomy. Our design emphasizes human-on-the-loop control where an operator expresses a desired high-level goal for which the reasoning component assembles an appropriate chain of subtasks. We introduce our work in the context of the DARPA Robotics Challenge (DRC) Finals. We describe the software architecture Team TROOPER developed and used to control an Atlas humanoid robot. We employ perception, planning, and control automation for execution of subtasks. If subtasks fail, or if changing environmental conditions invalidate the planned subtasks, the system automatically generates a new task chain. The operator is able to intervene at any stage of execution, to provide input and adjustment to any control layer, enabling operator involvement to increase as confidence in automation decreases. We present our performance at the DRC Finals and a discussion about lessons learned.
Steven Gray, Robert Chevalier, David Kotfis, Benjamin Caimano, Kenneth Chaney, Aron Rubin, Kingsley Fregene, Todd Danko

Team VALOR’s ESCHER: A Novel Electromechanical Biped for the DARPA Robotics Challenge

The Electric Series Compliant Humanoid for Emergency Response (ESCHER) platform represents the culmination of four years of development at Virginia Tech to produce a full sized force controlled humanoid robot capable of operating in unstructured environments. ESCHER’s locomotion capability was demonstrated at the DARPA Robotics Challenge (DRC) Finals when it successfully navigated the 61 m loose dirt course. Team VALOR, a Track A team, developed ESCHER leveraging and improving upon bipedal humanoid technologies implemented in previous research efforts, specifically for traversing uneven terrain and sustained untethered operation. This paper presents the hardware platform, software, and control systems developed to field ESCHER at the DRC Finals. ESCHER’s unique features include custom linear series elastic actuators (SEAs) in both single and dual actuator configurations and a whole-body control framework supporting compliant locomotion across variable and shifting terrain. A high-level software system designed using the Robot Operating System (ROS) integrated various open-source packages and interfaced with the existing whole-body motion controller. The paper discusses a detailed analysis of challenges encountered during the competition, along with lessons learned critical for transitioning research contributions to a fielded robot. Empirical data collected before, during, and after the DRC Finals validates ESCHER’s performance in fielded environments.
Coleman Knabe, Robert Griffin, James Burton, Graham Cantor-Cooke, Lakshitha Dantanarayana, Graham Day, Oliver Ebeling-Koning, Eric Hahn, Michael Hopkins, Jordan Neal, Jackson Newton, Chris Nogales, Viktor Orekhov, John Peterson, Michael Rouleau, John Seminatore, Yoonchang Sung, Jacob Webb, Nikolaus Wittenstein, Jason Ziglar, Alexander Leonessa, Brian Lattimer, Tomonari Furukawa

Perspectives on Human-Robot Team Performance from an Evaluation of the DARPA Robotics Challenge

The DARPA Robotics Challenge (DRC) was a competition designed to advance the capabilities of remotely teleoperated semi-autonomous humanoid robots performing in a disaster response scenario with degraded communications. Throughout the DRC, our evaluation team conducted two studies of human-robot interaction (HRI) for the Trials and Finals competitions. From these studies, we have generated recommendations and design guidelines for HRI with remote, semi-autonomous humanoids, but our findings also have implications outside of the competition’s domain. In this article, we discuss our perspectives on effective and ineffective human-robot teams based upon our experiences at the DRC. We consider the impact of various interfacing and control techniques, the effect of versatile robot design on task performance, and the operational context under which these factors work together to function in a human-centric environment. We use these underlying components of HRI to review how the advancements made at the DRC can be applied to present day robot applications and key capabilities for effective human-robot teams in the future.
Adam Norton, Willard Ober, Lisa Baraniecki, David Shane, Anna Skinner, Holly Yanco

What Happened at the DARPA Robotics Challenge Finals

This paper summarizes observations and lessons learned by the WPI-CMU team and self-reports made by many of the DARPA Robotics Challenge teams on what happened at the DARPA Robotics Challenge Finals. Major conclusions are: (1) Reducing operator errors is the most cost effective way to improve robot performance. Methods include operator training and practice, and software safeguards to detect and prevent operator errors. (2) Super-human sensing is another way to greatly improve robot performance. To some extent this matches what happened in the DARPA autonomous driving challenges, in which improved sensing was the key to improved performance. (3) Paradigm shifts are needed in academic robotics, such as emphasizing designing robust behaviors, systems design including what seem like unimportant issues such as thermal management, and consistent real world results rather than videos of the rare successes.
Christopher G. Atkeson, P. W. Babu Benzun, Nandan Banerjee, Dmitry Berenson, Christoper P. Bove, Xiongyi Cui, Mathew DeDonato, Ruixiang Du, Siyuan Feng, Perry Franklin, M. Gennert, Joshua P. Graff, Peng He, Aaron Jaeger, Joohyung Kim, Kevin Knoedler, Lening Li, Chenggang Liu, Xianchao Long, T. Padir, Felipe Polido, G. G. Tighe, X. Xinjilefu
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