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.     

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.     

虚拟现实技术下分拣机器人嵌入式遥控系统设计

为了解决当前遥控系统控制分拣机器人执行既定任务时存在目标轨迹跟踪效果不佳、跟踪误差较大,任务执行成功率较低、失误率较高以及系统响应延迟时间较长等缺点,提出并设计了基于虚拟现实技术的分拣机器人嵌入式遥控系统,研究通过分析虚拟现实系统组成结构基础上,将虚拟现实技术与分拣机器人技术有机结合,依据模块化、标准化、开放性和可用性原则,利用虚拟现实技术设计了具有临场感的操作指令输入输子系统和分拣机器人具有真实感的虚拟场景仿真子系统的嵌入式遥控系统;并给出了嵌入式遥控系统的基本功能单元和具体操作流程;依据该流程采用QNX Neutrino系统作为分拣机器人本体控制器,同时采用工业现场总线EtherCAT作为系统网络通信支撑,设计了嵌入式遥控系统软件控制程序,完成了基于虚拟现实技术的分拣机器人嵌入式遥控系统设计。模拟实验结果验证了设计系统的有效性,获得了较好的目标跟踪效果,减小了跟踪误差,提高了分拣机器人任务执行成功率,同时提高了系统效率。     

Efficient behavior learning in human–robot collaboration

We present a novel method for a robot to interactively learn, while executing, a joint human–robot task. We consider collaborative tasks realized by a team of a human operator and a robot helper that adapts to the human’s task execution preferences. Different human operators can have different abilities, experiences, and personal preferences so that a particular allocation of activities in the team is preferred over another. Our main goal is to have the robot learn the task and the preferences of the user to provide a more efficient and acceptable joint task execution. We cast concurrent multi-agent collaboration as a semi-Markov decision process and show how to model the team behavior and learn the expected robot behavior. We further propose an interactive learning framework and we evaluate it both in simulation and on a real robotic setup to show the system can effectively learn and adapt to human expectations.     

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 eight tasks and degraded communication conditions. This competition demanded various robotic technologies, such as manipulation, mobility, telemetry, autonomy, and localization. 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.     

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.     

A deep learning-enhanced Digital Twin framework for improving safety and reliability in human–robot collaborative manufacturing

In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human–robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our framework, a fully-supervised detector based on a faster region-based convolutional neural network is firstly trained on synthetic data generated by the Digital Twin, and then tested on the physical system to demonstrate the effectiveness of the proposed Digital Twin-based framework. To ensure safety and reliability, a semi-supervised detector is further designed to bridge the gap between the twin system and the physical system, and improved performance is achieved by the semi-supervised detector compared to the fully-supervised detector that is simply trained on either synthetic data or real data. The evaluation of the framework in multiple scenarios in which human operators collaborate with a Universal Robot 10 shows that it can accurately detect the human and robot, and classify their actions under a variety of conditions. The data from this evaluation have been made publicly available, and can be widely used for research and operational purposes. Additionally, a semi-automated annotation tool from the Digital Twin framework is published to benefit the collaborative robotics community.     

Backward Ladder Climbing Locomotion of Humanoid Robot with Gain Overriding Method on Position Control

We studied ladder climbing locomotion with the humanoid robot, DRC‐HUBO, under the constraints suggested by DARPA. Considering the hardware constraints of the robot platform, we planned for the robot to climb backward with four limbs moving separately. Task‐priority whole‐body inverse kinematics was used to generate and track the motion while maintaining COM inside the support polygon. As ladder climbing is a multicontact motion that generates interaction and internal forces, we resolved these issues using a gain overriding method applied to the position control of the motor controllers. This paper also provides various vision methods and posture modification strategies for the restricted conditions of the challenge. We ultimately verified our work in the DRC trials by getting a full score on the ladder task.     

Robotic grasping and manipulation through human visuomotor learning

A major goal of robotics research is to develop techniques that allow non-experts to teach robots dexterous skills. In this paper, we report our progress on the development of a framework which exploits human sensorimotor learning capability to address this aim. The idea is to place the human operator in the robot control loop where he/she can intuitively control the robot, and by practice, learn to perform the target task with the robot. Subsequently, by analyzing the robot control obtained by the human, it is possible to design a controller that allows the robot to autonomously perform the task. First, we introduce this framework with the ball-swapping task where a robot hand has to swap the position of the balls without dropping them, and present new analyses investigating the intrinsic dimension of the ball-swapping skill obtained through this framework. Then, we present new experiments toward obtaining an autonomous grasp controller on an anthropomorphic robot. In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot. The data collected is then analyzed for inferring the grasping strategy discovered by the human operator. Finally, a method to generalize grasping actions using the collected data is presented, which allows the robot to autonomously generate grasping actions for different orientations of the target object.     

Markerless human–robot interface for dual robot manipulators using Kinect sensor

Remote teleoperation of robot manipulators is often necessary in unstructured, dynamic, and dangerous environments. However, the existing mechanical and other contacting interfaces require unnatural, or hinder natural, human motions. At present, the contacting interfaces used in teleoperation for multiple robot manipulators often require multiple operators. Previous vision-based approaches have only been used in the remote teleoperation for one robot manipulator as well as require the special quantity of illumination and visual angle that limit the field of application. This paper presents a noncontacting Kinect-based method that allows a human operator to communicate his motions to the dual robot manipulators by performing double hand–arm movements that would naturally carry out an object manipulation task. This paper also proposes an innovative algorithm of over damping to solve the problem of error extracting and dithering due to the noncontact measure. By making full use of the human hand–arm motion, the operator would feel immersive. This human–robot interface allows the flexible implementation of the object manipulation task done in collaboration by dual robots through the double hand–arm motion by one operator.     

A model of software design

In this article we present a preliminary cognitive model of the process of software design. Our goal was to develop a model of expert problem-solving skills for a task in which domain knowledge played an extensive role. In our model the process of design is captured via goals-and-operators interacting with a knowledge base. We have defined the goals and operators as ones which are general to design, rather than specific to the current task. In addition, we have structured the atomic level operators so that they are able to access domain specific knowledge acquired through experience. This structure enables both general processes and domain specific knowledge to play critical roles in producing any particular design artifact. From our protocol analysis we have built a model which unites several recurring behaviors into an interpretable whole. the behaviors we account for include the building of mental models, mental simulation, and balanced development.     

An experimental study of adaptive testing for software reliability assessment

Adaptive testing is a new form of software testing that is based on the feedback and adaptive control principle and can be treated as the software testing counterpart of adaptive control. Our previous work has shown that adaptive testing can be formulated and guided in theory to minimize the variance of an unbiased software reliability estimator and to achieve optimal software reliability assessment. In this paper, we present an experimental study of adaptive testing for software reliability assessment, where the adaptive testing strategy, the random testing strategy and the operational profile based testing strategy were applied to the Space program in four experiments. The experimental results demonstrate that the adaptive testing strategy can really work in practice and may noticeably outperform the other two. Therefore, the adaptive testing strategy can serve as a preferable alternative to the random testing strategy and the operational profile based testing strategy if high confidence in the reliability estimates is required or the real-world operational profile of the software under test cannot be accurately identified.     

Adaptation technique for integrating genetic programming and reinforcement learning for real robots

We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) to enable a real robot to adapt its actions to a real environment. Our technique does not require a precise simulator because learning is achieved through the real robot. In addition, our technique makes it possible for real robots to learn effective actions. Based on this proposed technique, we acquire common programs, using GP, which are applicable to various types of robots. Through this acquired program, we execute RL in a real robot. With our method, the robot can adapt to its own operational characteristics and learn effective actions. In this paper, we show experimental results from two different robots: a four-legged robot "AIBO" and a humanoid robot "HOAP-1." We present results showing that both effectively solved the box-moving task; the end result demonstrates that our proposed technique performs better than the traditional Q-learning method.     

Life-long optimization of the symbolic model of indoor environments for a mobile robot.

The use of a symbolic model of the spatial environment becomes crucial for a mobile robot that is intended to operate optimally and intelligently in indoor scenarios. Constructing such a model involves important problems that are not solved completely at present. One is called anchoring, which implies to maintain a correct dynamic correspondence between the real world and the symbols in the model. The other problem is adaptation: among the numerous possible models that could be constructed for representing a given environment, optimization involves the selection of one that improves as much as possible the operations of the robot. To cope with both problems, in this paper, we propose a framework that allows an indoor mobile robot to learn automatically a symbolic model of its environment and to optimize it over time with respect to changes in both the environment and the robot operational needs through an evolutionary algorithm. For coping efficiently with the large amounts of information that the real world provides, we use abstraction, which also helps in improving task planning. Our experiments demonstrate that the proposed framework is suitable for providing an indoor mobile robot with a good symbolic model and adaptation capabilities.     

Human–robot communication for collaborative decision making — A probabilistic approach

Humans and robots need to exchange information if the objective is to achieve a task collaboratively. Two questions are considered in this paper: what and when to communicate. To answer these questions, we developed a human–robot communication framework which makes use of common probabilistic robotics representations. The data stored in the representation determines what to communicate, and probabilistic inference mechanisms determine when to communicate. One application domain of the framework is collaborative human–robot decision making: robots use decision theory to select actions based on perceptual information gathered from their sensors and human operators. In this paper, operators are regarded as remotely located, valuable information sources which need to be managed carefully. Robots decide when to query operators using Value-Of-Information theory, i.e. humans are only queried if the expected benefit of their observation exceeds the cost of obtaining it. This can be seen as a mechanism for adjustable autonomy whereby adjustments are triggered at run-time based on the uncertainty in the robots’ beliefs related to their task. This semi-autonomous system is demonstrated using a navigation task and evaluated by a user study. Participants navigated a robot in simulation using the proposed system and via classical teleoperation. Results show that our system has a number of advantages over teleoperation with respect to performance, operator workload, usability, and the users’ perception of the robot. We also show that despite these advantages, teleoperation may still be a preferable driving mode depending on the mission priorities.     

Flexible telemanipulation based handy robot teaching on tape masking with complex geometry

Traditional robot teaching methods are cumbersome, tedious and difficult to scale for high-mix low-volume applications. The tape masking, a common process for surface protection before plasma spraying, spray painting and shot peening, is one of those domains where robotic automation lacks flexibility and reliability due to the complexity in task. Fortunately, it is still within the grasps of human-robot collaborative systems. This work presents a telemanipulation-based robot teaching framework that is able to let the robot manipulator cope with the taping tasks with complex workpiece geometries. The proposed framework allows quick calibration, variable motion mapping, and indexing so that the operators can easily set up and guide the robotic taping system to cover the tapes onto the layers and grooves of different workpieces. This framework enables the operators to change the motion mapping scale for both large-scale guidance and fine motion dexterous manipulation. Meanwhile, an indexing function makes it possible for the operators to re-map their poses from the edges of their comfortable regions. A portable VR system is applied in the telemanipulation system. With its six DoF motion precisely measured in real-time, the proposed motion remapping algorithms enable the operators to directly guide the robot in their selected scales. Experimental results show that the proposed framework facilitates robot programming on the manipulation of the complex workpieces that have multi-layer surfaces and grooves in between. It also reduces the teaching time comparing to other methods. This system and method improve teaching efficiency and convenience, which has potential value to be deployed in manufacturing.     

基于出口遥感卫星移动接收站站监控软件设计

对于出口国际的遥感卫星移动接收站,站监控软件负责完成整站任务规划与调度、轨道预报、数据管理以及设备集中监视和控制;针对有人值守无人操作的遥感卫星移动接收站的运行特点和任务需求,制定遥感卫星数据接收优先级和任务筛选规则,按需采集地理位置信息,定时通过以太网获取最新卫星轨道数据,自动规划遥感卫星数据接收任务,利用任务节点矩阵驱动数据接收流程,自动化控制和调度移动站内各系统完成遥感卫星数据接收工作;在实际应用中提高了遥感卫星移动站的利用率,避免了人工操作降低系统运行效率,保障了遥感卫星数据接收的全覆盖,实现了有人值守无人操作的全自动化运行。     

基于ROS的云机器人服务框架

随着机器人技术的发展和硬件的普及,机器人的市场应用前景越来越广泛.但是,目前的机器人仍存在着许多局限,具体表现为可移植性弱,本地计算成本高,服务应用较少等.云机器人的提出,将机器人的计算能力从本地迁移到云端,不但提高了机器人的计算能力,降低了机器人硬件成本,而且能使资源的分配更为均衡,为解决机器人发展面临的困难提供了有效的解决途径.在云机器人的基础思想之上,提出一种基于ROS的云机器人服务框架.该服务框架使用开源的机器人操作系统ROS作为机器人运行的基础,增强了其对于不同硬件和软件环境的可移植性.同时,在框架的云端部分加入了机器人的服务管理系统和服务解析模块,能够方便快速地对机器人服务进行扩展和调用.在最后的实验部分,通过人脸识别服务模块对云机器人服务框架进行了实验验证.     

Robust variable admittance control for human–robot co-manipulation of objects with unknown load

Over the last years, physical Human–Robot Interaction (pHRI) has become a particularly interesting topic for industrial tasks. An important issue is allowing people and robots to collaborate in an useful, simple and safe manner. In this work, we propose a new framework that allows the person to collaborate with a robot manipulator, while the robot has its own predefined task. To allow the robot to smoothly switch from its own task to be a compliant collaborator for the person, a variable admittance control is developed. Furthermore, in general the task to accomplish requires the robot to carry variable, unknown, loads at the end-effector. To include this feature in our framework, a robust control is also included to preserve the performance of the robot despite uncertainties coming from the unknown load. To validate our approach, experiments were carried out with a Kuka LBR iiwa 14 R820, first to validate both parts of the controller, and finally, to study a use-case scenario similar to an industrial production line. Results show the efficiency of this approach to allow the person to collaborate at any moment while the robot is capable of performing another task. This flexible framework for object co-manipulation also allows unknown loads up to 2 kg to be handled without making the task more difficult for the person.     

Bilateral remote teaching and autonomous task execution with task progress feedback

This paper describes an approach to estimating the progress in a task executed by a humanoid robot and to synthesizing motion based on the current progress so that the robot can achieve the task. The robot observes a human performing whole body motion for a specific task, and encodes these motions into a hidden Markov model (HMM). The current observation is compared with the motion generated by the HMM, and the task progress can be estimated during the robot performing the motion. The robot subsequently uses the estimate of the task progress to generate a motion appropriate to the current situation with the feedback rule. We constructed a bilateral remote control system with humanoid robot HRP-4 and haptic device Novint Falcon, and we made the humanoid robot push a button. Ten trial motions of pushing a button were recorded for the training data. We tested our proposed approach on the autonomous execution of the pushing motion by the humanoid robot, and confirmed the effectiveness of our task progress feedback method.