Development of an Anthropomorphic Robotic Arm and Hand for Interactive Humanoids

Humanoid robots are designed and built to mimic human form and movement.Ultimately,they are meant to resemble the size and physical abilities of a human in order to function in human-oriented environments and to work autonomously but to pose no physical threat to humans.Here,a humanoid robot that resembles a human in appearance and movement is built using powerful actuators paired with gear trains,joint mechanisms,and motor drivers that are all encased in a package no larger than that of the human physique.In this paper,we propose the construction of a humanoid-applicable anthropomorphic 7-DoF arm complete with an 8-DoF hand.The novel mechanical design of this humanoid arm makes it sufficiently compact to be compatible with currently available narrating-model humanoids,and to be sufficiently powerful and flexible to be functional; the number of degrees of freedom endowed in this robotic arm is sufficient for executing a wide range of tasks,including dexterous hand movements.The developed humanoid arm and hand are capable of sensing and interpreting incoming external force using the motor in each joint current without conventional torque sensors.The humanoid arm adopts an algorithm to avoid obstacles and the dexterous hand is capable of grasping objects.The developed robotic arm is suitable for use in an interactive humanoid robot.     

Anthropomorphic Design of the Human-Like Walking Robot

In this paper, we present a new concept of the mechanical design of a humanoid robot. The goal is to build a humanoid robot utilizing a new structure which is more suitable for human-like walking with the characteristics of the knee stretch, heel-contact, and toe-off. Inspired by human skeleton, we made an anthropomorphic pelvis for the humanoid robot. In comparison with conventional humanoid robots, with such the anthropomorphic pelvis, our robot is capable of adjusting the center of gravity of the upper body by the motion of pelvic tilt, thus reducing the required torque at the ankle joint and the velocity variations in human-like walking. With more precise analysis of the foot mechanism, the fixed-length inverted pendulum can be used to describe the dynamics of biped walking, thus preventing redundant works and power consumption in length variable inverted pendulum system. As the result of the new structure we propose, a humanoid robot is able to walk with human-like gait.     

Moving just like you: motor interference depends on similar motility of agent and observer

Recent findings in neuroscience suggest an overlap between brain regions involved in the execution of movement and perception of another's movement. This so-called "action-perception coupling" is supposed to serve our ability to automatically infer the goals and intentions of others by internal simulation of their actions. A consequence of this coupling is motor interference (MI), the effect of movement observation on the trajectory of one's own movement. Previous studies emphasized that various features of the observed agent determine the degree of MI, but could not clarify how human-like an agent has to be for its movements to elicit MI and, more importantly, what 'human-like' means in the context of MI. Thus, we investigated in several experiments how different aspects of appearance and motility of the observed agent influence motor interference (MI). Participants performed arm movements in horizontal and vertical directions while observing videos of a human, a humanoid robot, or an industrial robot arm with either artificial (industrial) or human-like joint configurations. Our results show that, given a human-like joint configuration, MI was elicited by observing arm movements of both humanoid and industrial robots. However, if the joint configuration of the robot did not resemble that of the human arm, MI could longer be demonstrated. Our findings present evidence for the importance of human-like joint configuration rather than other human-like features for perception-action coupling when observing inanimate agents.     

An Efficient Gait-generating Method for Electrical Quadruped Robot Based on Humanoid Power Planning Approach

The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prompting.Minimizing energy consumption and keeping the stability of body are considered as two main characteristics of human walking.This work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking pattern.Based on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped robot.Through studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot prototype.The stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype experi-ment.The results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.     

Robot-Mediated Interviews - How Effective Is a Humanoid Robot as a Tool for Interviewing Young Children?

Robots have been used in a variety of education, therapy or entertainment contexts. This paper introduces the novel application of using humanoid robots for robot-mediated interviews. An experimental study examines how children’s responses towards the humanoid robot KASPAR in an interview context differ in comparison to their interaction with a human in a similar setting. Twenty-one children aged between 7 and 9 took part in this study. Each child participated in two interviews, one with an adult and one with a humanoid robot. Measures include the behavioural coding of the children’s behaviour during the interviews and questionnaire data. The questions in these interviews focused on a special event that had recently taken place in the school. The results reveal that the children interacted with KASPAR very similar to how they interacted with a human interviewer. The quantitative behaviour analysis reveal that the most notable difference between the interviews with KASPAR and the human were the duration of the interviews, the eye gaze directed towards the different interviewers, and the response time of the interviewers. These results are discussed in light of future work towards developing KASPAR as an ‘interviewer’ for young children in application areas where a robot may have advantages over a human interviewer, e.g. in police, social services, or healthcare applications.     

Rhythm Patterns Interaction - Synchronization Behavior for Human-Robot Joint Action

Interactive behavior among humans is governed by the dynamics of movement synchronization in a variety of repetitive tasks. This requires the interaction partners to perform for example rhythmic limb swinging or even goal-directed arm movements. Inspired by that essential feature of human interaction, we present a novel concept and design methodology to synthesize goal-directed synchronization behavior for robotic agents in repetitive joint action tasks. The agents’ tasks are described by closed movement trajectories and interpreted as limit cycles, for which instantaneous phase variables are derived based on oscillator theory. Events segmenting the trajectories into multiple primitives are introduced as anchoring points for enhanced synchronization modes. Utilizing both continuous phases and discrete events in a unifying view, we design a continuous dynamical process synchronizing the derived modes. Inverse to the derivation of phases, we also address the generation of goal-directed movements from the behavioral dynamics. The developed concept is implemented to an anthropomorphic robot. For evaluation of the concept an experiment is designed and conducted in which the robot performs a prototypical pick-and-place task jointly with human partners. The effectiveness of the designed behavior is successfully evidenced by objective measures of phase and event synchronization. Feedback gathered from the participants of our exploratory study suggests a subjectively pleasant sense of interaction created by the interactive behavior. The results highlight potential applications of the synchronization concept both in motor coordination among robotic agents and in enhanced social interaction between humanoid agents and humans.     

Comparison of human and humanoid robot control of upright stance

There is considerable recent interest in developing humanoid robots. An important substrate for many motor actions in both humans and biped robots is the ability to maintain a statically or dynamically stable posture. Given the success of the human design, one would expect there are lessons to be learned in formulating a postural control mechanism for robots. In this study we limit ourselves to considering the problem of maintaining upright stance. Human stance control is compared to a suggested method for robot stance control called zero moment point (ZMP) compensation. Results from experimental and modeling studies suggest there are two important subsystems that account for the low- and mid-frequency (DC to 1 Hz) dynamic characteristics of human stance control. These subsystems are (1) a “sensory integration” mechanism whereby orientation information from multiple sensory systems encoding body kinematics (i.e. position, velocity) is flexibly combined to provide an overall estimate of body orientation while allowing adjustments (sensory re-weighting) that compensate for changing environmental conditions and (2) an “effort control” mechanism that uses kinetic-related (i.e., force-related) sensory information to reduce the mean deviation of body orientation from upright. Functionally, ZMP compensation is directly analogous to how humans appear to use kinetic feedback to modify the main sensory integration feedback loop controlling body orientation. However, a flexible sensory integration mechanism is missing from robot control leaving the robot vulnerable to instability in conditions where humans are able to maintain stance. We suggest the addition of a simple form of sensory integration to improve robot stance control. We also investigate how the biological constraint of feedback time delay influences the human stance control design. The human system may serve as a guide for improved robot control, but should not be directly copied because the constraints on robot and human control are different.     

Interactive language learning by robots: the transition from babbling to word forms

The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that may contribute to this process, a key factor being the sensitivity of learners to the statistical distribution of linguistic elements. As well as being necessary for learning word meanings, the acquisition of anchor word forms facilitates the segmentation of an acoustic stream through other mechanisms. In our experiments some salient one-syllable word forms are learnt by a humanoid robot in real-time interactions with naive participants. Words emerge from random syllabic babble through a learning process based on a dialogue between the robot and the human participant, whose speech is perceived by the robot as a stream of phonemes. Numerous ways of representing the speech as syllabic segments are possible. Furthermore, the pronunciation of many words in spontaneous speech is variable. However, in line with research elsewhere, we observe that salient content words are more likely than function words to have consistent canonical representations; thus their relative frequency increases, as does their influence on the learner. Variable pronunciation may contribute to early word form acquisition. The importance of contingent interaction in real-time between teacher and learner is reflected by a reinforcement process, with variable success. The examination of individual cases may be more informative than group results. Nevertheless, word forms are usually produced by the robot after a few minutes of dialogue, employing a simple, real-time, frequency dependent mechanism. This work shows the potential of human-robot interaction systems in studies of the dynamics of early language acquisition.     

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

We investigate the role of obstacle avoidance in visually guided reaching and grasping movements. We report on a human study in which subjects performed prehensile motion with obstacle avoidance where the position of the obstacle was systematically varied across trials. These experiments suggest that reaching with obstacle avoidance is organized in a sequential manner, where the obstacle acts as an intermediary target. Furthermore, we demonstrate that the notion of workspace travelled by the hand is embedded explicitly in a forward planning scheme, which is actively involved in detecting obstacles on the way when performing reaching. We find that the gaze proactively coordinates the pattern of eye–arm motion during obstacle avoidance. This study provides also a quantitative assessment of the coupling between the eye–arm–hand motion. We show that the coupling follows regular phase dependencies and is unaltered during obstacle avoidance. These observations provide a basis for the design of a computational model. Our controller extends the coupled dynamical systems framework and provides fast and synchronous control of the eyes, the arm and the hand within a single and compact framework, mimicking similar control system found in humans. We validate our model for visuomotor control of a humanoid robot.     

Brain Response to a Humanoid Robot in Areas Implicated in the Perception of Human Emotional Gestures

Background

The humanoid robot WE4-RII was designed to express human emotions in order to improve human-robot interaction. We can read the emotions depicted in its gestures, yet might utilize different neural processes than those used for reading the emotions in human agents.

Methodology

Here, fMRI was used to assess how brain areas activated by the perception of human basic emotions (facial expression of Anger, Joy, Disgust) and silent speech respond to a humanoid robot impersonating the same emotions, while participants were instructed to attend either to the emotion or to the motion depicted.

Principal Findings

Increased responses to robot compared to human stimuli in the occipital and posterior temporal cortices suggest additional visual processing when perceiving a mechanical anthropomorphic agent. In contrast, activity in cortical areas endowed with mirror properties, like left Broca''s area for the perception of speech, and in the processing of emotions like the left anterior insula for the perception of disgust and the orbitofrontal cortex for the perception of anger, is reduced for robot stimuli, suggesting lesser resonance with the mechanical agent. Finally, instructions to explicitly attend to the emotion significantly increased response to robot, but not human facial expressions in the anterior part of the left inferior frontal gyrus, a neural marker of motor resonance.

Conclusions

Motor resonance towards a humanoid robot, but not a human, display of facial emotion is increased when attention is directed towards judging emotions.

Significance

Artificial agents can be used to assess how factors like anthropomorphism affect neural response to the perception of human actions.     

Biologically inspired kinematic synergies enable linear balance control of a humanoid robot

Despite many efforts, balance control of humanoid robots in the presence of unforeseen external or internal forces has remained an unsolved problem. The difficulty of this problem is a consequence of the high dimensionality of the action space of a humanoid robot, due to its large number of degrees of freedom (joints), and of non-linearities in its kinematic chains. Biped biological organisms face similar difficulties, but have nevertheless solved this problem. Experimental data reveal that many biological organisms reduce the high dimensionality of their action space by generating movements through linear superposition of a rather small number of stereotypical combinations of simultaneous movements of many joints, to which we refer as kinematic synergies in this paper. We show that by constructing two suitable non-linear kinematic synergies for the lower part of the body of a humanoid robot, balance control can in fact be reduced to a linear control problem, at least in the case of relatively slow movements. We demonstrate for a variety of tasks that the humanoid robot HOAP-2 acquires through this approach the capability to balance dynamically against unforeseen disturbances that may arise from external forces or from manipulating unknown loads.     

Human-like Manipulation Planning for Articulated Manipulator

A new method based on human-likeness assessment and optimization concept to solve the problem of human-like manipulation planning for articulated robot is proposed in this paper.This method intrinsically formulates the problem as a constrained optimization problem in robot configuration space.The robot configuration space is divided into different subregions by human likeness assessment.A widely used strategy,Rapid Upper Limb Assessment (RULA) in applied ergonomics,is adopted here to evaluate the human likeness of robot configuration.A task compatibility measurement of the robot velocity transmission ratio along a specified direction is used as the target function for the optimization problem.Simple illustrative examples of this method applied to a two Degree of Freedom (DOF) planar robot that resembles the upper limb of a human are presented.Further applications to a humanoid industrial robot SDA10D are also presented.The reasonable planning results for these applications assert the effectiveness of our method.     

Development of neural mechanisms for machine learning

The goal of this work is to develop a humanoid robot's perceptual mechanisms through the use of learning aids. We describe methods to enable learning on a humanoid robot using learning aids such as books, drawing materials, boards, educational videos or other children toys. Visual properties of objects are learned and inserted into a recognition scheme, which is then applied to acquire new object representations - we propose learning through developmental stages. Inspired in infant development, we will also boost the robot's perceptual capabilities by having a human caregiver performing educational and play activities with the robot (such as drawing, painting or playing with a toy train on a railway). We describe original algorithms to extract meaningful percepts from such learning experiments. Experimental evaluation of the algorithms corroborates the theoretical framework.     

Vestibular humanoid postural control

Many of our motor activities require stabilization against external disturbances. This especially applies to biped stance since it is inherently unstable. Disturbance compensation is mainly reactive, depending on sensory inputs and real-time sensor fusion. In humans, the vestibular system plays a major role. When there is no visual space reference, vestibular-loss clearly impairs stance stability. Most humanoid robots do not use a vestibular system, but stabilize upright body posture by means of center of pressure (COP) control. We here suggest using in addition a vestibular sensor and present a biologically inspired vestibular sensor along with a human-inspired stance control mechanism. We proceed in two steps. First, in an introductory review part, we report on relevant human sensors and their role in stance control, focusing on own models of transmitter fusion in the vestibular sensor and sensor fusion in stance control. In a second, experimental part, the models are used to construct an artificial vestibular system and to embed it into the stance control of a humanoid. The robot’s performance is investigated using tilts of the support surface. The results are compared to those of humans. Functional significance of the vestibular sensor is highlighted by comparing vestibular-able with vestibular-loss states in robot and humans. We show that a kinematic body-space sensory feedback (vestibular) is advantageous over a kinetic one (force cues) for dynamic body-space balancing. Our embodiment of human sensorimotor control principles into a robot is more than just bionics. It inspired our biological work (neurorobotics: ‘learning by building’, proof of principle, and more). We envisage a future clinical use in the form of hardware-in-the-loop simulations of neurological symptoms for improving diagnosis and therapy and designing medical assistive devices.     

From 'understanding the brain by creating the brain' towards manipulative neuroscience

Ten years have passed since the Japanese 'Century of the Brain' was promoted, and its most notable objective, the unique 'creating the brain' approach, has led us to apply a humanoid robot as a neuroscience tool. Here, we aim to understand the brain to the extent that we can make humanoid robots solve tasks typically solved by the human brain by essentially the same principles. I postulate that this 'Understanding the Brain by Creating the Brain' approach is the only way to fully understand neural mechanisms in a rigorous sense. Several humanoid robots and their demonstrations are introduced. A theory of cerebellar internal models and a systems biology model of cerebellar synaptic plasticity is discussed. Both models are experimentally supported, but the latter is more easily verifiable while the former is still controversial. I argue that the major reason for this difference is that essential information can be experimentally manipulated in molecular and cellular neuroscience while it cannot be manipulated at the system level. I propose a new experimental paradigm, manipulative neuroscience, to overcome this difficulty and allow us to prove cause-and-effect relationships even at the system level.     

Design of a robotic gait trainer using spring over muscle actuators for ankle stroke rehabilitation

Repetitive task training is an effective form of rehabilitation for people suffering from debilitating injuries of stroke. We present the design and working concept of a robotic gait trainer (RGT), an ankle rehabilitation device for assisting stroke patients during gait. Structurally based on a tripod mechanism, the device is a parallel robot that incorporates two pneumatically powered, double-acting, compliant, spring over muscle actuators as actuation links which move the ankle in dorsiflex ion/plantarflexion and inversion/eversion. A unique feature in the tripod design is that the human anatomy is part of the robot, the first fixed link being the patient's leg. The kinematics and workspace of the tripod device have been analyzed determining its range of motion. Experimental gait data from an able-bodied person wearing the working RGT prototype are presented.     

Motor Contagion during Human-Human and Human-Robot Interaction

Motor resonance mechanisms are known to affect humans'' ability to interact with others, yielding the kind of “mutual understanding” that is the basis of social interaction. However, it remains unclear how the partner''s action features combine or compete to promote or prevent motor resonance during interaction. To clarify this point, the present study tested whether and how the nature of the visual stimulus and the properties of the observed actions influence observer''s motor response, being motor contagion one of the behavioral manifestations of motor resonance. Participants observed a humanoid robot and a human agent move their hands into a pre-specified final position or put an object into a container at various velocities. Their movements, both in the object- and non-object- directed conditions, were characterized by either a smooth/curvilinear or a jerky/segmented trajectory. These trajectories were covered with biological or non-biological kinematics (the latter only by the humanoid robot). After action observation, participants were requested to either reach the indicated final position or to transport a similar object into another container. Results showed that motor contagion appeared for both the interactive partner except when the humanoid robot violated the biological laws of motion. These findings suggest that the observer may transiently match his/her own motor repertoire to that of the observed agent. This matching might mediate the activation of motor resonance, and modulate the spontaneity and the pleasantness of the interaction, whatever the nature of the communication partner.     

Artificial Sweat for Humanoid Finger

To achieve favorable Frictional Tactile Sensation （FTS） for robot and humanoid fingers, this report investigated the effects of human finger sweat on Friction Coefficient （FC） and verified the effectiveness of artificial sweat on FTS tbr a humanoid finger. The results show that the model sweat （salt and urea water faked real sweat） increases the FC of the real finger sliding on the high hygroscopic and rough surface （paper）, whereas on the low hygroscopic and smooth surface （PMMA）, the sweat forms a fluid film and decreases FC, restricting severe finger adhesion. Further, the film formation and capillary adhesion force of sweat were discussed. The experimental results with the artificial sweats （ethanol and water） and humanoid finger （silicone rubber skin with tactile sensors） verifies the effectiveness. The artificial sweat restricts severe adhesion （stick-slip vibration）, and enhances cognitive capability of FTS.     

Comparative Study of SSVEP- and P300-Based Models for the Telepresence Control of Humanoid Robots

In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot—a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments concerning the open-loop and closed-loop control of a humanoid robot via brain signals. The visual stimuli of both the SSVEP- and P300- based models were implemented on a LCD computer monitor with a refresh frequency of 60 Hz. Considering the operation safety, we set the classification accuracy of a model over 90.0% as the most important mandatory for the telepresence control of the humanoid robot. The open-loop experiments demonstrated that the SSVEP model with at most four stimulus targets achieved the average accurate rate about 90%, whereas the P300 model with the six or more stimulus targets under five repetitions per trial was able to achieve the accurate rates over 90.0%. Therefore, the four SSVEP stimuli were used to control four types of robot behavior; while the six P300 stimuli were chosen to control six types of robot behavior. Both of the 4-class SSVEP and 6-class P300 models achieved the average success rates of 90.3% and 91.3%, the average response times of 3.65 s and 6.6 s, and the average information transfer rates (ITR) of 24.7 bits/min 18.8 bits/min, respectively. The closed-loop experiments addressed the telepresence control of the robot; the objective was to cause the robot to walk along a white lane marked in an office environment using live video feedback. Comparative studies reveal that the SSVEP model yielded faster response to the subject’s mental activity with less reliance on channel selection, whereas the P300 model was found to be suitable for more classifiable targets and required less training. To conclude, we discuss the existing SSVEP and P300 models for the control of humanoid robots, including the models proposed in this paper.     

Design Study of a Cable-based Gait Training Machine

This paper deals with a design approach of a gait training machine based on a quantitative gait analysis.The proposed training machine is composed of a body weight support device and a cable-driven parallel robot.This paper is focused on the cable-driven robot,which controls the pose of the lower limb through an orthosis placed on the patient's leg.The cable robot reproduces a normal gait movement through the motion of the orthosis.A motion capture system is used to perform the quantitative analysis of a normal gait,which will be used as an input to the inverse dynamic model of the cable robot.By means of an optimization algorithm,the optimal design parameters,which minimize the tensions in the cables,are determined.Two constraints are considered,i.e.,a non-negative tension in the cables at all times,and a free cable/end-effector collision.Once the optimal solution is computed,a power analysis is carried out in order to size the robot actuators.The proposed approach can be easily extended for the design study of a similar type of cable robots.