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.     

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 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.     

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.     

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.     

Interacting with team oriented plans in multi-robot systems

Team oriented plans have become a popular tool for operators to control teams of autonomous robots to pursue complex objectives in complex environments. Such plans allow an operator to specify high level directives and allow the team to autonomously determine how to implement such directives. However, the operators will often want to interrupt the activities of individual team members to deal with particular situations, such as a danger to a robot that the robot team cannot perceive. Previously, after such interrupts, the operator would usually need to restart the team plan to ensure its success. In this paper, we present an approach to encoding how interrupts can be smoothly handled within a team plan. Building on a team plan formalism that uses Colored Petri Nets, we describe a mechanism that allows a range of interrupts to be handled smoothly, allowing the team to efficiently continue with its task after the operator intervention. We validate the approach with an application of robotic watercraft and show improved overall efficiency. In particular, we consider a situation where several platforms should travel through a set of pre-specified locations, and we identify three specific cases that require the operator to interrupt the plan execution: (i) a boat must be pulled out; (ii) all boats should stop the plan and move to a pre-specified assembly position; (iii) a set of boats must synchronize to traverse a dangerous area one after the other. Our experiments show that the use of our interrupt mechanism decreases the time to complete the plan (up to 48 % reduction) and decreases the operator load (up to 80 % reduction in number of user actions). Moreover, we performed experiments with real robotic platforms to validate the applicability of our mechanism in the actual deployment of robotic watercraft.     

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.     

Structural plan similarity based on refinements in the space of partial plans

Plan similarity measures play a key role in many areas of artificial intelligence, such as case‐based planning, plan recognition, ambient intelligence, or digital storytelling. In this paper, we present 2 novel structural similarity measures to compare plans based on a search process in the space of partial plans. Partial plans are compact representations of sets of plans with some common structure and can be organized in a lattice so that the most general partial plans are above the most specific ones. To compute our similarity measures, we traverse this space of partial plans from the most general to the most specific using successive refinements. Our first similarity measure is designed for propositional plan formalisms, and the second is designed for classical planning formalisms (including variables and types). We also introduce 2 novel refinement operators used to traverse the space of plans: an ideal downward refinement operator for propositional partial plans and a finite and complete downward refinement operator for classical partial plans. Finally, we evaluate our similarity measures in the context of a nearest neighbor classifier using 2 datasets commonly used in the plan recognition literature (Linux and Monroe), showing good results in both synthetic and real data.     

A new paradigm of humanoid robot motion programming based on touch interpretation

Most humanoid soccer robot teams design the basic movements of their robots, like walking and kicking, off-line and manually. Once these motions are considered satisfactory, they are stored in the robot’s memory and played according to a high level behavioral strategy. Much time is spent in the development of the movements, and despite the significant progress made in humanoid soccer robots, the interfaces employed for the development of motions are still quite primitive. In order to accelerate development, an intuitive instruction method is desired. We propose the development of robot motions through physical interaction. In this paper we propose a ”teaching by touching” approach; the human operator teaches a motion by directly touching the robot’s body parts like a dance instructor. Teaching by directly touching is intuitive for instructors. However, the robot needs to interpret the instructor’s intention since tactile communication can be ambiguous. This paper presents a method to learn the interpretation of the touch meaning and investigates, through experiments, a general (shared among different users) and intuitive touch manner.     

Visual servo walking control for humanoids with finite-time convergence and smooth robot velocities

In this paper, we address the problem of humanoid locomotion guided from information of a monocular camera. The goal of the robot is to reach a desired location defined in terms of a target image, i.e., a positioning task. The proposed approach allows us to introduce a desired time to complete the positioning task, which is advantageous in contrast to the classical exponential convergence. In particular, finite-time convergence is achieved while generating smooth robot velocities and considering the omnidirectional waking capability of the robot. In addition, we propose a hierarchical task-based control scheme, which can simultaneously handle the visual positioning and the obstacle avoidance tasks without affecting the desired time of convergence. The controller is able to activate or inactivate the obstacle avoidance task without generating discontinuous velocity references while the humanoid is walking. Stability of the closed loop for the two task-based control is demonstrated theoretically even during the transitions between the tasks. The proposed approach is generic in the sense that different visual control schemes are supported. We evaluate a homography-based visual servoing for position-based and image-based modalities, as well as for eye-in-hand and eye-to-hand configurations. The experimental evaluation is performed with the humanoid robot NAO.     

Gait design for an ice skating humanoid robot

Basic walking gaits are a common building block for many activities in humanoid robotics, such as robotic soccer. The nature of the walking surface itself also has a strong affect on an appropriate gait. Much work is currently underway in improving humanoid walking gaits by dealing with sloping, debris-filled, or otherwise unstable surfaces. Travel on slippery surfaces such as ice, for example, greatly increases the potential speed of a human, but reduces stability. Humans can compensate for this lack of stability through the adaptation of footwear such as skates, and the development of gaits that allow fast but controlled travel on such footwear.This paper describes the development of a gait to allow a small humanoid robot to propel itself on ice skates across a smooth surface, and includes work with both ice skates and inline skates. The new gait described in this paper relies entirely on motion in the frontal plane to propel the robot, and allows the robot to traverse indoor and outdoor ice surfaces more stably than a classic inverted pendulum-based walking gait when using the same skates. This work is demonstrated using Jennifer, a modified Robotis DARwIn-OP humanoid robot with 20 degrees of freedom.     

A closed-loop approach for tracking a humanoid robot using particle filtering and depth data

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.     

Realizing whole-body tactile interactions with a self-organizing,multi-modal artificial skin on a humanoid robot

In this paper, we present a new approach to realize whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot. We, therefore, equipped the whole upper body of the humanoid HRP-2 with various patches of CellulARSkin – a modular artificial skin. In order to automatically handle a potentially high number of tactile sensor cells and motors units, the robot uses open-loop exploration motions, and distributed accelerometers in the artificial skin cells, to acquire its self-centered sensory-motor knowledge. This body self-knowledge is then utilized to transfer multi-modal tactile stimulations into reactive body motions. Tactile events provide feedback on changes of contact on the whole-body surface. We demonstrate the feasibility of our approach on a humanoid, here HRP-2, grasping large and unknown objects only via tactile feedback. Kinesthetically taught grasping trajectories, are reactively adapted to the size and stiffness of different test objects. Our paper contributes the first realization of a self-organizing tactile sensor-behavior mapping on a full-sized humanoid robot, enabling a position controlled robot to compliantly handle objects.     

不同个性的情感机器人表情研究

在情感机器人研究中,不同个性的面部表情是情感机器人增强真实感的重要基础。为实现情感机器人更加丰富细腻的表情,将人类的个性特征引入情感机器人,分析个性理论和情感模型理论,得知不同个性机器人的情感强度。结合面部动作编码系统中面部表情与机器人控制点之间的映射关系,得到情感机器人不同个性的基本表情实现方法。利用Solidworks建立情感机器人脸部模型,在ANSYS工程软件中将SHFR-Ⅲ情感机器人脸部模型设置为弹性体,通过有限元仿真计算方法,对表情的有限元仿真方法进行了探究,得到实现SHFR-Ⅲ不同个性基本表情的控制区域载荷大小和仿真结果。最后,根据仿真结果,进行SHFR-Ⅲ情感机器人不同个性的表情动作实验。实验结果表明,有限元表情仿真可以指导SHFR-Ⅲ情感机器人实现近似人类的不同个性的基本面部表情。     

Autonomous knowledge acquisition based on artificial curiosity: Application to mobile robots in an indoor environment

This paper describes an autonomous system for knowledge acquisition based on artificial curiosity. The proposed approach allows a humanoid robot to discover, in an indoor environment, the world in which it evolves, and to learn autonomously new knowledge about it. The learning process is accomplished by observation and by interaction with a human tutor, based on a cognitive architecture with two levels. Experimental results of deployment of this system on a humanoid robot in a real office environment are provided. We show that our cognitive system allows a humanoid robot to gain increased autonomy in matters of knowledge acquisition.     

Robot Catching: Towards Engaging Human-Humanoid Interaction

Our focus is on creating interesting and human-like behaviors for humanoid robots and virtual characters. Interactive behaviors are especially engaging. They are also challenging, as they necessitate finding satisfactory realtime solutions for complex systems such as the 30-degree-of-freedom humanoid robot in our laboratory. Here we describe a catching behavior between a person and a robot. We generate ball-hand impact predictions based on the flight of the ball, and human-like motion trajectories to move the hand to the catch position. We use a dynamical systems approach to produce the motion trajectories where new movements are generated from motion primitives as they are needed.     

Learning to Walk by Imitation in Low-Dimensional Subspaces

In this paper, we provide the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data without any prior information of its dynamics model. Programming a humanoid robot to perform an action (such as walking) that takes into account the robot's complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex (and often brittle) control algorithms for generating a stable dynamic motion. Training using human mocap is an intuitive and flexible approach to programming a robot, but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using high-dimensional mocap data in the humanoid full-body joint space is typically intractable. We propose a new approach to tractable imitation-based learning in humanoids without a robot's dynamic model. We represent kinematic information from human mocap in a low-dimensional subspace and map motor commands in this low-dimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate optimal motor commands that satisfy the initial kinematic constraints as best as possible while generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking learned from mocap data using both a simulator and a real humanoid robot.     

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently.The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.