DARPA Robotics Challenge Finals Research Articles

A Shared Control Method for Collaborative Human-Robot Plug Task

Humans are superior to robots in performing complex assembly tasks primarily due to their dexterity and sensorimotor abilities. When humans and robots collaborate to complete an assembly task, it is critical to provide the robot with sufficiently precise manipulation capabilities and formulate a shared control method that coordinates human and robot actions. In this study, we are investigating human-robot collaboration in completing an assembly task, namely the plug task. The plug task, motivated by the 2015 DARPA Robotics Challenge Finals, involves inserting a plug connected with a cable into a paired socket. The human holds the socket while the robot is grasping the cable and manages to insert the plug. We estimate an initial socket pose based on human subjects' data. With the statistically calculated initial guess, the robot could act instantly from the beginning to move the plug towards an initial target. The actual socket pose is updated using the Kalman filter in real-time. Besides, the system continuously updates the cable model and tracks the plug to enable visual servoing to complete the plug task. The result of an extensive experimental study demonstrates that the proposed approach demonstrates fast and accurate performance as well as flexibility when compared to a state-of-the-art method based on interactive probabilistic movement primitives.

Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World

Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach.

Ground Vehicle Driving by Full Sized Humanoid

This paper presents a technical overview of the Team DRC-Hubo@UNLV’s approach to the driving task in DARPA Robotics Challenge Finals (DRC-Finals). First, hardware updates in the main body and the perception head of the contestant robot, DRC-Hubo+, were presented by comparison with the previous platform (DRC-Hubo in DRC-Trials). Then, the control system which enabled the full-sized humanoid to drive the off-the-shelf utility vehicle in the competition was provided. For this, the sensor data fusion process and the advanced driving assistant techniques were emphasized. Next, the driving pattern analysis of other type operators (such as human-drivers or pure tele-operation systems) was provided to demonstrate the performance of the developed system. Lastly, test and evaluation of the built robot and control system was provided via experimentation which DRC-Hubo+ drove the vehicle in a real-world setting. The presented approach was also verified in DRC-Finals as the author’s team was placed in the top record at the driving task in the competition.

WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments

In this work, we present WALK‐MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, 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 actuator drives with unique performance features that differentiate the robot from previous state‐of‐the‐art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK‐MAN actuation that combines customized high‐performance modules with tuned torque/velocity curves and transmission elasticity for high‐speed adaptation response and motion reactions to disturbances. WALK‐MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK‐MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before 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 behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion 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 is 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 teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.

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 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 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 that are critical for transitioning research contributions to a fielded robot. Empirical data collected before, during, and after the DRC Finals validate ESCHER's performance in fielded environments.

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

This paper presents a technical overview of Team DRC‐Hubo@UNLV's 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 paper. 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.

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 it 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 we 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 in 3rd place out 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.

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.

Team WPI‐CMU: Achieving Reliable Humanoid Behavior in the DARPA Robotics Challenge

In the DARPA Robotics Challenge (DRC), participating human‐robot teams were required to integrate mobility, manipulation, perception, and operator interfaces to complete a simulated disaster mission. We describe our approach using 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.

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. These plans 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 along with an analysis of each task.

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.

DRC Finals 2015에서 휴머노이드 로봇의 자동차 운전과 하차에 관한 전략

This paper presents various strategies for humanoid vehicle driving and egress tasks. For driving, a tele-operating system that controls a robot based on a human operator’s commands is built. In addition, an autonomous assistant module is developed for the operator. Normal position control can result in severe damage to robots when they egress from vehicles. To prevent this problem, another approach that mixes various joint control techniques is adopted in this study. Additionally, a footplate is newly designed and attached to the vehicle floor for the ground landing phase of the egress task. The attached plate enables the robot to step down onto the ground in a safe manner. For stable locomotion, a balance controller is designed for the humanoid. For the design of the controller, the robot is modeled using an inverted pendulum that consists of a spring and a damper. Then, a state feedback controller (with pole placement and a state observer) is built based on the simplified model. Many approaches that are presented in this paper were successfully applied to a full-sized humanoid, DRC-HUBO+, in the DARPA Robotics Challenge Finals, which were held in the United States in 2015.

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 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.

Effective teleoperated manipulation for humanoid robots in partially unknown real environments: team AIST-NEDO’s approach for performing the Plug Task during the DRC Finals

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

This paper 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 an 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.

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.

DARPAロボティクスチャレンジ決勝戦でのロボットシステム開発と教訓

DARPAロボティクスチャレンジ決勝戦でのロボットシステム開発と教訓

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.