Showing papers on "Humanoid robot published in 2015"

Whole-body model-predictive control applied to the HRP-2 humanoid

Abstract: Controlling the robot with a permanently-updated optimal trajectory, also known as model predictive control, is the Holy Grail of whole-body motion generation. Before obtaining it, several challenges should be faced: computation cost, non-linear local minima, algorithm stability, etc. In this paper, we address the problem of applying the updated optimal control in real-time on the physical robot. In particular, we focus on the problems raised by the delays due to computation and by the differences between the real robot and the simulated model. Based on the optimal-control solver MuJoCo, we implemented a complete model-predictive controller and we applied it in real-time on the physical HRP-2 robot. It is the first time that such a whole-body model predictive controller is applied in real-time on a complex dynamic robot. Aside from the technical contributions cited above, the main contribution of this paper is to report the experimental results of this premiere implementation.

Valkyrie: NASA's First Bipedal Humanoid Robot

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

Actuator Control for the NASA-JSC Valkyrie Humanoid Robot: A Decoupled Dynamics Approach for Torque Control of Series Elastic Robots

Abstract: This paper discusses the actuator-level control of Valkyrie, a new humanoid robot designed by NASA's Johnson Space Center in collaboration with several external partners. Several topics pertaining to Valkyrie's series elastic actuators are presented including control architecture, controller design, and implementation in hardware. A decentralized approach is taken in controlling Valkyrie's many series elastic degrees of freedom. By conceptually decoupling actuator dynamics from robot limb dynamics, the problem of controlling a highly complex system is simplified and the controller development process is streamlined compared to other approaches. This hierarchical control abstraction is realized by leveraging disturbance observers in the robot's joint-level torque controllers. A novel analysis technique is applied to understand the ability of a disturbance observer to attenuate the effects of unmodeled dynamics. The performance of this control approach is demonstrated in two ways. First, torque tracking performance of a single Valkyrie actuator is characterized in terms of controllable torque resolution, tracking error, bandwidth, and power consumption. Second, tests are performed on Valkyrie's arm, a serial chain of actuators, to demonstrate the robot's ability to accurately track torques with the presented decentralized control approach.

Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids

Abstract: While a lot of progress has recently been made in dynamic motion planning for humanoid robots, much of this work has remained limited to simulation. Here we show that executing the resulting trajectories on a Darwin-OP robot, even with local feedback derived from the optimizer, does not result in stable movements. We then develop a new trajectory optimization method, adapting our earlier CIO algorithm to plan through ensembles of perturbed models. This makes the plan robust to model uncertainty, and leads to successful execution on the robot. We obtain a high rate of task completion without trajectory divergence (falling) in dynamic forward walking, sideways walking, and turning, and a similarly high success rate in getting up from the floor (the robot broke before we could quantify the latter). Even though the planning is still done offline, the present work represents a significant step towards automating the tedious scripting of complex movements.

A Novel EOG/EEG Hybrid Human–Machine Interface Adopting Eye Movements and ERPs: Application to Robot Control

Abstract: This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interface works in two modes: an EOG mode recognizes eye movements such as blinks, and an EEG mode detects event related potentials (ERPs) like P300. While both eye movements and ERPs have been separately used for implementing assistive interfaces, which help patients with motor disabilities in performing daily tasks, the proposed hybrid interface integrates them together. In this way, both the eye movements and ERPs complement each other. Therefore, it can provide a better efficiency and a wider scope of application. In this study, we design a threshold algorithm that can recognize four kinds of eye movements including blink, wink, gaze, and frown. In addition, an oddball paradigm with stimuli of inverted faces is used to evoke multiple ERP components including P300, N170, and VPP. To verify the effectiveness of the proposed system, two different online experiments are carried out. One is to control a multifunctional humanoid robot, and the other is to control four mobile robots. In both experiments, the subjects can complete tasks effectively by using the proposed interface, whereas the best completion time is relatively short and very close to the one operated by hand.

When Children Teach a Robot to Write: An Autonomous Teachable Humanoid Which Uses Simulated Handwriting

Abstract: This article presents a novel robotic partner which children can teach handwriting. The system relies on the learning by teaching paradigm to build an interaction, so as to stimulate meta-cognition, empathy and increased self-esteem in the child user. We hypothesise that use of a humanoid robot in such a system could not just engage an unmotivated student, but could also present the opportunity for children to experience physically-induced benefits encountered during human-led handwriting interventions, such as motor mimicry. By leveraging simulated handwriting on a synchronised tablet display, a NAO humanoid robot with limited fine motor capabilities has been configured as a suitably embodied handwriting partner. Statistical shape models derived from principal component analysis of a dataset of adult-written letter trajectories allow the robot to draw purposefully deformed letters. By incorporating feedback from user demonstrations, the system is then able to learn the optimal parameters for the appropriate shape models. Preliminary in situ studies have been conducted with primary school classes to obtain insight into children's use of the novel system. Children aged 6-8 successfully engaged with the robot and improved its writing to a level which they were satisfied with. The validation of the interaction represents a significant step towards an innovative use for robotics which addresses a widespread and socially meaningful challenge in education.

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

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

Adaptive Neural Control for Dual-Arm Coordination of Humanoid Robot With Unknown Nonlinearities in Output Mechanism

Abstract: To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov’s stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

Can Robotic Interaction Improve Joint Attention Skills

Abstract: Although it has often been argued that clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorder (ASD), relatively few investigations have indexed the impact of intervention and feedback approaches. This pilot study investigated the application of a novel robotic interaction system capable of administering and adjusting joint attention prompts to a small group (n = 6) of children with ASD. Across a series of four sessions, children improved in their ability to orient to prompts administered by the robotic system and continued to display strong attention toward the humanoid robot over time. The results highlight both potential benefits of robotic systems for directed intervention approaches as well as potent limitations of existing humanoid robotic platforms.

iCub Whole-Body Control through Force Regulation on Rigid Non-Coplanar Contacts

Abstract: This paper details the implementation on the humanoid robot iCub of state-of-the-art algorithms for whole-body control. We regulate the forces between the robot and its surrounding environment to stabilize a desired robot posture. We assume that the forces and torques are exerted on rigid contacts. The validity of this assumption is guaranteed by constraining the contact forces and torques, e.g. the contact forces must belong to the associated friction cones. The implementation of this control strategy requires to estimate the external forces acting on the robot, and the internal joint torques. We then detail algorithms to obtain these estimations when using a robot with an iCub-like sensor set, i.e. distributed six-axis force-torque sensors and whole-body tactile sensors. A general theory for identifying the robot inertial parameters is also presented. From an actuation standpoint, we show how to implement a joint torque control in the case of DC brushless motors. In addition, the coupling mechanism of the iCub torso is investigated. The soundness of the entire control architecture is validated in a real scenario involving the robot iCub balancing and making contacts at both arms.

Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Abstract: We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

Analysis of Human-robot Interaction at the DARPA Robotics Challenge Trials

Abstract: In December 2013, the Defense Advanced Research Projects Agency DARPA Robotics Challenge DRC Trials were held in Homestead, Florida. The DRC Trials were designed to test the capabilities of humanoid robots in disaster response scenarios with degraded communications. Each team created their own interaction method to control their robot, either the Boston Dynamics Atlas robot or a robot built by the team itself. Of the 15 competing teams, eight participated in our study of human-robot interaction. We observed the participating teams from the field with the robot and in the control room with the operators, noting many performance metrics, such as critical incidents and utterances, and categorizing their interaction methods according to the number of operators, control methods, and amount of interaction. We decomposed each task into a series of subtasks, different from the DRC Trials official subtasks for points, to gain a better understanding of each team's performance in varying complexities of mobility and manipulation. Each team's interaction methods have been compared to their performance, and correlations have been analyzed to understand why some teams ranked higher than others. We discuss lessons learned from this study, and we have found in general that the guidelines for human-robot interaction for unmanned ground vehicles still hold true: more sensor fusion, fewer operators, and more automation lead to better performance.

Measuring empathy for human and robot hand pain using electroencephalography.

Abstract: This study provides the first physiological evidence of humans’ ability to empathize with robot pain and highlights the difference in empathy for humans and robots. We performed electroencephalography in 15 healthy adults who observed either human- or robot-hand pictures in painful or non-painful situations such as a finger cut by a knife. We found that the descending phase of the P3 component was larger for the painful stimuli than the non-painful stimuli, regardless of whether the hand belonged to a human or robot. In contrast, the ascending phase of the P3 component at the frontal-central electrodes was increased by painful human stimuli but not painful robot stimuli, though the interaction of ANOVA was not significant, but marginal. These results suggest that we empathize with humanoid robots in late top-down processing similarly to human others. However, the beginning of the top-down process of empathy is weaker for robots than for humans.

Optimization based controller design and implementation for the Atlas robot in the DARPA Robotics Challenge Finals

Abstract: We describe the design and hardware implementation of our walking and manipulation controllers that are based on a cascade of online optimizations. A virtual force acting at the robot's center of mass (CoM) is estimated and used to compensated for modeling errors of the CoM and unplanned external forces. The proposed controllers have been implemented on the Atlas robot, a full size humanoid robot built by Boston Dynamics, and used in the DARPA Robotics Challenge Finals, which consisted of a wide variety of locomotion and manipulation tasks.

Development of life-sized high-power humanoid robot JAXON for real-world use

Abstract: This paper presents the development of life-sized high-power humanoid robot JAXON. Humanoid robots for disaster relief assistance need the same degree of physical performance as humans. We have developed STARO as the high-power humanoid robot with a high degree of physical performance. However this is not enough for practical use of the humanoid robot in a disaster site. We consider the following as additional conditions to operate humanoid robots for disaster relief assistance outside of the lab in outdoor environments. 1) Robots have humanlike body proportion to work in infrastructure matched to human body structure. 2) Robots have energy sources such as batteries and act without tethers. 3) Robots walk with two legs or four limbs and continue to work without fatal damage in unexpected rollover. JAXON satisfied these conditions. We demonstrates the performance of JAXON through the experiment of getting out of a vehicle, stepping over walls, and operating on batteries. Further more, we assesses the performance of the strong armor and the shock absorbing structure through a backward over-turning accident.

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

Abstract: 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 cont...

Trajectory generation for multi-contact momentum control

Abstract: Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on linear quadratic regulator (LQR) design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control.

Using a Humanoid Robot to Elicit Body Awareness and Appropriate Physical Interaction in Children with Autism

Abstract: In this article we describe a human–robot interaction study, focusing on tactile aspects of interaction, in which children with autism interacted with the child-like humanoid robot KASPAR. KASPAR was equipped with touch sensors in order to be able to distinguish gentle from harsh touch, and to respond accordingly. The study investigated a novel scenario for robot-assisted play, with the goal to increase body awareness of children with autism spectrum condition (hereafter ASC) by teaching them how to identify human body parts, and to promote a triadic relationship between the child, the robot and the experimenter. Data obtained from the video analysis of the experimental sessions showed that children treated KASPAR as an object of shared attention with the experimenter, and performed more gentle touches on the robot along the sessions. The children also learned to identify body parts. The study showed the potential that teaching children with autism about body parts and appropriate physical interaction using a humanoid robot has, and highlighted the issues of scenario development, data collection and data analysis that will inform future studies.

Long-term LEGO therapy with humanoid robot for children with ASD

Abstract: To utilise the knowledge gained from highly specialised domains as autism therapy to robot-based interactive training platforms, an innovative design approach is needed. We present the process of content creation and co-design of LEGO therapy for children with autism spectrum disorders performed by a humanoid robot. The co-creation takes place across the disciplines of autism therapy, and behavioural robotics, and applies methods from design and human-robot interaction, in order to connect state-of-the-art developments in these disciplines. We designed, carried out and analyzed a pilot and final experiment, in which a robot mediated LEGO therapy between pairs of children was mediated by a robot over the course of 10 to 12 sessions. The impact of the training on the children was then analysed from a clinical and human-robot interaction perspective. Our major findings are as follows: first, game-based robot scenarios in which the game continues over the sessions opened possibilities for long-term interventions using robots and led to a significant increase in social initiations during the intervention in natural settings; and second, including dyadic interactions between robot and child within triadic games with robots has positive effects on the children's engagement and on creating learning moments that comply with the chosen therapy framework.

Stability of surface contacts for humanoid robots: Closed-form formulae of the Contact Wrench Cone for rectangular support areas

Abstract: Humanoids locomote by making and breaking contacts with their environment. Thus, a crucial question for them is to anticipate whether a contact will hold or break under effort. For rigid surface contacts, existing methods usually consider several point-contact forces, which has some drawbacks due to the underlying redundancy. We derive a criterion, the Contact Wrench Cone (CWC), which is equivalent to any number of applied forces on the contact surface, and for which we provide a closed-form formula. It turns out that the CWC can be decomposed into three conditions: (i) Coulomb friction on the resultant force, (ii) CoP inside the support area, and (iii) upper and lower bounds on the yaw torque. While the first two are well-known, the third one is novel. It can, for instance, be used to prevent the undesired foot yaws observed in biped locomotion. We show that our formula yields simpler and faster computations than existing approaches for humanoid motions in single support, and assess its validity in the OpenHRP simulator.

Complete Analytical Forward and Inverse Kinematics for the NAO Humanoid Robot

Abstract: The design of complex dynamic motions for humanoid robots is achievable only through the use of robot kinematics. In this paper, we study the problems of forward and inverse kinematics for the Aldebaran NAO humanoid robot and present a complete, exact, analytical solution to both problems, including a software library implementation for real-time on-board execution. The forward kinematics allow NAO developers to map any configuration of the robot from its own joint space to the three-dimensional physical space, whereas the inverse kinematics provide closed-form solutions to finding joint configurations that drive the end effectors of the robot to desired target positions in the three-dimensional physical space. The proposed solution was made feasible through a decomposition into five independent problems (head, two arms, two legs), the use of the Denavit-Hartenberg method, the analytical solution of a non-linear system of equations, and the exploitation of body and joint symmetries. The main advantage of the proposed inverse kinematics solution compared to existing approaches is its accuracy, its efficiency, and the elimination of singularities. In addition, we suggest a generic guideline for solving the inverse kinematics problem for other humanoid robots. The implemented, freely-available, NAO kinematics library, which additionally offers center-of-mass calculations and Jacobian inverse kinematics, is demonstrated in three motion design tasks: basic center-of-mass balancing, pointing to a moving ball, and human-guided balancing on two legs.

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

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

Continuous humanoid locomotion over uneven terrain using stereo fusion

Abstract: For humanoid robots to fulfill their mobility potential they must demonstrate reliable and efficient locomotion over rugged and irregular terrain. In this paper we present the perception and planning algorithms which have allowed a humanoid robot to use only passive stereo imagery (as opposed to actuating a laser range sensor) to safely plan footsteps to continuously walk over rough and uneven surfaces without stopping. The perception system continuously integrates stereo imagery to build a consistent 3D model of the terrain which is then used by our footstep planner which reasons about obstacle avoidance, kinematic reachability and foot rotation through mixed-integer quadratic optimization to plan the required step positions. We illustrate that our stereo imagery fusion approach can measure the walking terrain with sufficient accuracy that it matches the quality of terrain estimates from LIDAR. To our knowledge this is the first such demonstration of the use of computer vision to carry out general purpose terrain estimation on a locomoting robot — and additionally to do so in continuous motion. A particular integration challenge was ensuring that these two computationally intensive systems operate with minimal latency (below 1 second) to allow re-planning while walking. The results of extensive experimentation and quantitative analysis are also presented. Our results indicate that a laser range sensor is not necessary to achieve locomotion in these challenging situations.

Humanoid robot HRP-2Kai — Improvement of HRP-2 towards disaster response tasks

Abstract: This paper presents a humanoid robot HRP-2Kai, which is the improvement version of HRP-2 towards disaster response tasks. HRP-2 stands for a humanoid robotics platform-2, which was developed in phase two of the Japanese national project HRP (Humanoid Robotics Project, from 1998FY to 2002FY), while Kai means improvement in Japanese. In a year of the ninth year from releasing HRP-2, the Great East Japan Earthquake shook Japan on March 11, 2011. Since we reflected on that we were not able to deploy our robots for emergency response at that time, we started a study of the disaster response humanoid robots by improving HRP-2. Improvements are presented with its basic specification in this paper.

Why Some Humanoid Faces Are Perceived More Positively Than Others: Effects of Human-Likeness and Task

Abstract: Ample research in social psychology has highlighted the importance of the human face in human-human interactions. However, there is a less clear understanding of how a humanoid robot's face is perceived by humans. One of the primary goals of this study was to investigate how initial perceptions of robots are influenced by the extent of human-likeness of the robot's face, particularly when the robot is intended to provide assistance with tasks in the home that are traditionally carried out by humans. Moreover, although robots have the potential to help both younger and older adults, there is limited knowledge of whether the two age groups' perceptions differ. In this study, younger (N = 32) and older adults (N = 32) imagined interacting with a robot in four different task contexts and rated robot faces of varying levels of human-likeness. Participants were also interviewed to assess their reasons for particular preferences. This multi-method approach identified patterns of perceptions across different appearances as well as reasons that influence the formation of such perceptions. Overall, the results indicated that people's perceptions of robot faces vary as a function of robot human-likeness. People tended to over-generalize their understanding of humans to build expectations about a human-looking robot's behavior and capabilities. Additionally, preferences for humanoid robots depended on the task although younger and older adults differed in their preferences for certain humanoid appearances. The results of this study have implications both for advancing theoretical understanding of robot perceptions and for creating and applying guidelines for the design of robots.

A closed-form solution for real-time ZMP gait generation and feedback stabilization

Abstract: Here we present a closed-form solution to the continuous time-varying linear-quadratic regulator problem for zero-moment point (ZMP) tracking. This generalizes previous analytical solutions for gait generation by allowing "soft" tracking (with a quadratic cost) of the desired ZMP, and by providing the feedback gains for the resulting time-varying optimal controller. This enables very fast O(n) computation, with n the number of piecewise polynomial segments in the desired ZMP trajectory. Results are presented using the Atlas humanoid robot where dynamic walking is achieved by recomputing the optimal controller online.

Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to the iCub's answers.

Abstract: To investigate the functional and social acceptance of a humanoid robot, we carried out an experimental study with 56 adult participants and the iCub robot. Trust in the robot has been considered as a main indicator of acceptance in decision-making tasks characterized by perceptual uncertainty (e.g., evaluating the weight of two objects) and socio-cognitive uncertainty (e.g., evaluating which is the most suitable item in a specific context), and measured by the participants' conformation to the iCub's answers to specific questions. In particular, we were interested in understanding whether specific (i) user-related features (i.e. desire for control), (ii) robot-related features (i.e., attitude towards social influence of robots), and (iii) context-related features (i.e., collaborative vs. competitive scenario), may influence their trust towards the iCub robot. We found that participants conformed more to the iCub's answers when their decisions were about functional issues than when they were about social issues. Moreover, the few participants conforming to the iCub's answers for social issues also conformed less for functional issues. Trust in the robot's functional savvy does not thus seem to be a pre-requisite for trust in its social savvy. Finally, desire for control, attitude towards social influence of robots and type of interaction scenario did not influence the trust in iCub. Results are discussed with relation to methodology of HRI research.

Human-Like Behavior Generation Based on Head-Arms Model for Robot Tracking External Targets and Body Parts

Abstract: Facing and pointing toward moving targets is a usual and natural behavior in daily life. Social robots should be able to display such coordinated behaviors in order to interact naturally with people. For instance, a robot should be able to point and look at specific objects. This is why, a scheme to generate coordinated head-arm motion for a humanoid robot with two degrees-of-freedom for the head and seven for each arm is proposed in this paper. Specifically, a virtual plane approach is employed to generate the analytical solution of the head motion. A quadratic program (QP)-based method is exploited to formulate the coordinated dual-arm motion. To obtain the optimal solution, a simplified recurrent neural network is used to solve the QP problem. The effectiveness of the proposed scheme is demonstrated using both computer simulation and physical experiments.

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

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

Revisiting the body-schema concept in the context of whole-body postural-focal dynamics.

Abstract: The body-schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert) but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the equilibrium-point hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory–motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability.