Learning from Demonstrations(LfD) enables robots to acquire complex skills by observing human behavior, significantly reducing the need for explicit programming. However, applying LfD in industrial settings remains challenging due to limited demonstrations, variability in task executions, and the need to generalize across diverse scenarios. To address these issues, this paper presents a learning based hierarchical task and motion planning framework that integrates Behavior Trees (BT) for high-level task sequencing and Dynamic Motion Primitives (DMP) for low-level motion generation. Demonstration trajectories are segmented and actions are generated using an agentcentric, state-augmented segmentation strategy. Subsequently, relevant features are automatically extracted to define the pre-and post-conditions for each action for the construction of a modular BT. For motion execution, DMP are enhanced with a recovery mechanism for adaptive, obstacle-aware reproduction. A backchaining mechanism is also introduced for BT extension. Validation was performed through simulation and real-world experiments on multiple tasks. Comparative results demonstrate that the proposed method outperforms existing LfD and planning baselines in task success rate, efficiency, and motion smoothness, highlighting its potential for flexible and scalable automation.

Measuring individual characteristics related to Human-Agent Interaction (HAI) poses a fundamental challenge in developing personalized HAI systems. This study used an industrial collaborative environment as a testbed to evaluate the effectiveness of measuring general personality traits using the Big Five Inventory-2 versus preferences for collaborative robot behaviors. A survey of 114 participants revealed that collaboration styles naturally cluster into two distinct types: "tool-oriented" users who prioritize efficiency, and "partner-oriented" users who emphasize relational aspects with agents. Machine learning classifiers were developed to predict these collaboration styles, yielding accuracies of 56.0% when using general personality traits versus 95.7%±3.9% (5-fold cross-validation) with only three questions. The developed classifier shows potential for application beyond industrial settings to personal adaptation system design across diverse HAI domains, including adaptive educational agents and personal healthcare AI systems.

This paper explores a novel interaction paradigm where an expressive Augmented Reality (AR) avatar is physically embodied by a concealed robotic arm, enabling it to manipulate real-world objects. The central challenge in making this paradigm viable is maintaining the user’s sense of presence by ensuring the actuator remains completely concealed. We designed a new interaction system and propose a key occlusion algorithm that effectively conceal the physical robotic arm from view, allowing users to perceive only the expressive virtual avatar. This approach demonstrates the potential of a new Human-Robot Interaction paradigm and opens up possibilities for everyday service robots, educational applications, and remote presence systems where low-host and highly customizable expressiveness is essential.

Autonomous mobile robots that operate in human environments are expected to navigate in ways that feel safe to people. Proxemics—a concept of interpersonal distance—has a potential to support this goal. Previous studies have proposed proxemic-based navigation using single human cues, such as emotion or posture. However, relying on a single cue may be insufficient to accurately estimate human intent, limiting the potential for socially acceptable robot navigation. In this study, we present an engagement-aware navigation framework that dynamically adapts proxemic distance by incorporating multiple cues, including facial expressions, body and head orientation, and gaze. In an experiment replicating a daily-life scenario, results indicated that our method enhances perceived intelligence compared to a constant-distance approach, particularly for users with little or no prior robot experience. Furthermore, experienced users exhibited different comfort responses, suggesting that prior robot experience significantly influences human-robot proxemics.

In the current era of AI technology, where systems increasingly rely on big data to process vast amounts of societal information, efficient methods for integrating and utilizing diverse datasets are essential. This article presents a novel approach for transforming the feature space of different datasets through singular value decomposition (SVD) to extract common and hidden features as using the prior domain knowledge. Specifically, we apply this method to two datasets: 1) one related to physical and cognitive frailty in the elderly; and 2) another focusing on identifyingIKIGAI(happiness, self-efficacy, and sense of contribution) in volunteer staff of a civic health promotion activity. Both datasets consist of multiple sub-datasets measured using different modalities, such as facial expressions, sound, activity, and heart rates. By defining feature extraction methods for each subdataset, we compare and integrate the overlapping data. The results demonstrated that our method could effectively preserve common characteristics across different data types, offering a more interpretable solution than traditional dimensionality reduction methods based on linear and nonlinear transformation. This approach has significant implications for data integration in multidisciplinary fields and opens the door for future applications to a wide range of datasets.

Teachable robots in education have the ability to increase student engagement and learning through personalised interactions and the use of social behaviours, such as speech, gaze and emotional expressions. Adaptation of these behaviours is motivated by an interest in tailoring the learning experience to an individual user and improving user outcomes. This work proposes an adaptive response-selection algorithm for a teachable robot which aims to increase user task engagement. A Q-learning algorithm learns an individualised policy and is rewarded based on the user’s paraphrasing behaviour and response time when teaching the robot. A user study is conducted across two user groups, recruited from Australia and Japan. This study evaluates the performance of the adaptive approach for response selection against a non-adaptive method and explores the differences in response and perception of the teaching task between the two participant groups. The results show that measures of task engagement increase more when using the adaptive approach compared to a non-adaptive method of response selection, but that this difference is not consistent across both participant groups. The adaptive approach is also shown to have a positive effect on user perceptions of the interaction.

Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot’s unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N = 35) where the participant played a “catch-falling-coins” game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an “active session” where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants’ understanding of the robot’s “intention” and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot’s “intention” was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot’s intention. We also observed relevant relationships between some personality traits and a person’s understanding of the robot’s intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human’s understanding of unanticipated robot actions and their positive or negative perception of the robot.

Feeling together and at the same time feeling free while sharing the same roof is a balance that newly retired couples try to reach. Indeed the beginning of retirement is complex, and sometimes, even when both spouses find themselves at home together, some spouses could experience a feeling of loneliness. To respond to this insight, we introduce the concept of “sensible presence robject” - Yōkobo to fill this loneliness gap through subtle interactions. The pictorial introduces and describes the different steps of the design process of Yōkobo as a non-anthropomorphic and non-vocal robot for the entrance of dwellings. Through its expressiveness, Yōkobo is a presence messenger for newly retired couples. On a larger scale, this research is a manifesto for the slow technology trend in which perceptions and time open a discussion on poetic sensibility.

Our research aims at understanding if robots could be good at public speaking, what they need to achieve the level of a good public speaker and how they may surpass a human public speaker. Previous research results indicate that designing a robot speaker by mimicking some of the behaviours of a human speaker is not enough to create an effective robot speech performance. It can in fact be counter productive to strive for human-likeliness. In this paper, we describe how we programmed a toy-like, non-anthropomorphic small robot (the Anki Vector robot) to deliver a speech by extracting pose and facial expression information from the video of a human speaker and loosely retargeting this information to the robot. We also describe our experimental plans to compare Vector’s speech delivery performance with the performance of a more anthropomorphic robot (the SoftBank Pepper robot), which has been programmed to closely mimic the human speaker’s behaviour. They are compared in terms of their ability to evoke the positive affective responses necessary to spark interest, motivate the audience to listen, and engage the audience in meaningful ways.

Unanticipated physical actions from the robot on humans [active physical human–robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a physically and psychologically safe manner. The objective of this article is to explore the possibility of quantifying the humans’ physical and mental state during an active physical interaction with a robot, by means of a laboratory experiment. We hypothesize that the active robot actions could cause measurable alterations in users’ data, which could be related to their perceptions and personalities. In the experiment, the user plays a visual game using the robot, which has a hidden task that results in active physical actions on the user. We collect data from physical and physiological sensors, and the perceptions and personalities via questionnaires and a semi-structured interview. Statistical analysis and clustering of the data collected from a total of 35 participants showed the relationships between participants’ physical and physiological data and their age, gender, perception, and personalities. Further developments based on these exploratory outcomes can be used to implement an active pHRI controller that can account for both the physical and the mental state of users.

This article presents a novel software architecture enabling the analysis of assembly actions from fine-grained hand motions. Unlike previous works that compel humans to wear ad-hoc devices or visual markers in the human body, our approach enables users to move without additional burdens. Modules developed are able to: (i) reconstruct the 3D motions of body and hands keypoints using multi-camera systems; (ii) recognize objects manipulated by humans, and (iii) analyze the relationship between the human motions and the manipulated objects. We implement different solutions based on OpenPose and Mediapipe for body and hand keypoint detection. Additionally, we discuss the suitability of these solutions for enabling real-time data processing. We also propose a novel method using Long Short-Term Memory (LSTM) deep neural networks to analyze the relationship between the detected human motions and manipulated objects. Experimental validations show the superiority of the proposed approach against previous works based on Hidden Markov Models (HMMs).

Research in biomechanics hypothesizes that human motion is optimal with respect to an unknown cost function that varies depending on the action and/or task. This unknown cost function is often approximated as the weighted sum of a set of features or basis cost functions. As a person performs a sequence of actions, the weights associated to each of these basis functions are likely to vary over time. Given a human demonstration and the corresponding cost weight trajectory recovered via inverse optimal control (IOC), this paper proposes an optimal control (OC) method that can generate robot motion based on human movement using time-varying cost function weights. By using time-varying weights, the proposed optimal control method can handle changing optimization criteria without segmentation. The method is evaluated both in simulation and with recorded human data. Using human demonstration data, we demonstrate the reproduction of pick-and-place motions with an average end-effector error at the pick place location within 0.82 cm, which is significantly lower than the average trajectory error, indicating that the approach correctly prioritizes reaching the pick and place locations without manual segmentation.

Human motion retargeting to humanoid robots (i.e., transferring motion data to robots for human imitation) is a challenging process with many potential real-work applications. However, current state-of-the-art frameworks present practical limitations, such as the requirement of camera calibration and the implementation of expensive equipment for motion capture. Therefore, we propose a novel framework for motion retargeting based on a single-view camera and human pose estimation. Unlike previous works, our framework is cost and computationally efficient, and it is applicable both on prerecorded uncalibrated videos and web-camera live streams. The framework is composed of three modules: 1) 2D coordinate extraction from the integrated Google BlazePose, 2) 3D modeling by depth estimation using a geometrical algorithm, and 3) human joint angles computation and input process to Pepper robot. Pepper’s imitation accuracy is evaluated qualitatively by direct motion similarity observation and quantitatively by comparison between output and input motion data to observe the effect of Pepper’s physical limitations. Results suggest that our proposed framework is able to reproduce human-like motion sequences, however with some limitations due to the hardware.

Studies have shown that the appearance and movements of home robots may play key roles in the impression and engagement of users, opposed to recent rises in the smart speaker market. In this research, we conduct a user experiment with the aim of clarifying the elements required to evaluate the human impression of a robot's movements, based on the hypothesis that adequate movements may lead to better impressions and engagement. We compare the impressions of participants who interacted with a robot with movements (behavior robot) and a robot without movements (non-behavior robot). Results show that when using the behavior robot, participants showed significantly higher values in their impressions of cheerfulness and sociability. Questionnaires about interaction revealed that personalization is also an important function for robots to make a good impression on humans.

Social robots acting as stand-ins for speakers or teachers would enable them to reach large audiences from anywhere in the world, increasing the options for distant learning. They would need to be endowed with effective public speaking skills though, in order to deliver their message, entertain, and maintain audience attention.

This paper describes our ongoing work to develop a visual sensing platform that can inform a robot teacher about the behaviour and affective state of its student audience. We have developed a multi-student behaviour recognition system, which can detect behaviours such as "listening" to the lecturer, "raising hand", or "sleeping". We have also developed a multi-student affect recognition system which, starting from eight basic emotions detected from facial expressions, can infer higher emotional states relevant to a learning context, such as "interested", "distracted" and "confused". Both systems are being tested with the Softbank robot Pepper that can respond to various students' behaviours and emotional states with adapted movements, postures and speech.

In child-robot interaction, the element of trust towards the robot is critical. This is particularly important the first time the child meets the robot, as the trust gained during this interaction can play a decisive role in future interactions. We present an in-the-wild study where Polish kindergartners interacted with a Pepper robot. The videos of this study were analyzed for the issues of trust, anthropomorphization, and reaction to malfunction, with the assumption that the last two factors influence the children's trust towards Pepper. Our results reveal children's interest in the robot performing tasks specific for humans, highlight the importance of the conversation scenario and the need for an extended library of answers provided by the robot about its abilities or origin and show how children tend to provoke the robot.

Robots that have different forms and capabilities are used in a wide variety of situations; however, one common point to all robots interacting with humans is their ability to communicate with them. In addition to verbal communication or purely communicative movements, robots can also use their embodiment to generate expressive movements while achieving a task, to convey additional information to its human partner. This article surveys state-of-the-art techniques that generate whole-body expressive movements in robots and robot avatars. We consider different embodiments such as wheeled, legged, or flying systems and the different metrics used to evaluate the generated movements. Finally, we discuss future areas of improvement and the difficulties to overcome to develop truly expressive motions in artificial agents.

Knowledge of the mass and inertial parameters of a humanoid robot is crucial for the development of model-based controller and motion planning in dynamics situation. Parameters are usually provided from Computer Aided Design (CAD) data and thus inaccurate specially if the robot is modified over time. In this paper, a practical method consisting of hanging a humanoid robot to a fix force sensor to perform its dynamic identification is proposed. This allows, contrary to the literature, to generate very exciting and dynamic motions to identify most of the elements of the inertia tensors in a reduced amount of time. This procedure transforms an instable floating base legged humanoid robot to a safe fix base tree structure robot which makes easier to generate optimal exciting motions. Because of a better excitation the overall trajectory lasts for less than a minute. The method was experimentally validated with a HOAP3 humanoid robot and using a 6-axis force sensor. A reduction of 3 times in average of the RMS difference between measured external reaction forces and moments and their estimates from CAD data was obtained with a single minute of optimal exciting motions.

This paper presents a novel optimization method for generating persistently exciting trajectories for inertial parameters identification of a robot. The exciting performance of the trajectories is usually evaluated by the condition number of the regressor matrix, which appears in the linear regression model for identification. In this paper, the efficient formulation is presented to directly compute the gradient of the condition number with respect to joint trajectory parameters, by deriving the derivative of the singular values and regressor matrices. Direct gradient computation can enhance computational performance of optimization, which is essential for large DOF systems under many physical consistent conditions such as humanoid robots. The proposed method is validated by generating several trajectories for the humanoid robot HRP-4.

The rapid advancement of robotics poses the problem of a deep integration of robotic systems in human environments. In order to achieve this symbiosis between humans and robots, the artificial systems have to take into account one of the most important aspects in human life: emotions. The recognition and understanding of human emotions is crucial for robotic systems to behave in appropriate ways according to the situation and smoothly integrate with all the different aspects of human life. This paper proposes a novel algorithm which uses state-of-the-art techniques in Machine Learning, in particular Recurrent Neural Networks, to automatically infer emotional clues from non-stylized motions (i.e. motions which are not supposed to convey emotional information as primary goal). This algorithm recognized human emotions with an accuracy between 0.68 and 0.80, depending on the considered motion, and clearly overcomes human capacity in the same task for the considered cases studied. Since the implemented algorithm is able to perform online, its results can be used to allow a behavioural programming which gives the robot the flexibility to act in a more human-oriented way.

Some studies in the field of HRI show that the impressions given by a robot depends on motions. The robot that is developed for communication with human needs to give the appropriate impression of the users. Yet robot motion gives a different impression to the observers. This means that robot designers and programmers have to make trial and errors to generate behaviors conveying the appropriate impressions. This study developed a predictive model of user's qualitative impression scores, and evaluates the effectiveness of the model using experimental data. In the experiment, 2 kinds of humanoid robots presented 6 behaviors and participants rated the qualitative impression. We compared the obtained scores with the predictive scores calculated by our model. As a result, it is possible to predict users' impression scores for robot behaviors is 89.6% of the cases.

The “social learning and multimodal interaction for designing artificial agents” workshop aims at presenting scientific and philosophical advances related to social learning and multimodal interaction for enhancing the design of artificial agents. Papers presented in the workshop include studies on human behavior modeling, on social robotics and on virtual agents. Our two invited speakers, Prof. Catherine Pelachaud and Prof. Louis-Philippe Morency will enrich and open the door to further discussion by bringing their widely acknowledged expertise in the field.

Knowledge of the mass and inertial parameters of a humanoid robot or a human being is crucial for the development of model-based control, as well as for monitoring the rehabilitation process. These parameters are also important for obtaining realistic simulations in the field of motion planning and human motor control. For robots, they are often provided by computer-aided design data, while averaged anthropometric table values are often used for human subjects. The unit/subject-specific inertial parameters can be identified by using the external wrench caused by the ground reaction. However, the identification accuracy intrinsically depends on the excitation properties of the recorded motion. In this paper, a new method for obtaining optimal excitation motions is proposed. This method is based on the identification model of legged systems and on optimization processes to generate excitation motions while handling mechanical constraints. A pragmatic decomposition of this problem, the use of a new excitation criterion, and a quadratic program to identify inertial parameters are proposed. The method has been experimentally validated onto an HOAP-3 humanoid robot and with one human subject.

We propose a method for estimation of humanoid and human links' inertial parameters. Our approach formulates the problem as a hierarchical quadratic program by exploiting the linear properties of rigid body dynamics with respect to the inertia parameters. In order to assess our algorithm, we conducted experiments with a humanoid robot and a human subject. We compared ground reaction forces and moments estimated from force measurements with those computed using identified inertia parameters and movement information. Our method is able to accurately reconstruct ground reaction forces and force moments. Moreover, our method is able to estimate correctly masses of the robots links and to accurately detect additional masses placed on the human subject during the experiments.

The thirteen papers in this special section focus on the topic of movement science for humans and humanoids. The papers include the collection and organization of human movement data for enabling robotics research; the use of human movement as inspiration for humanoid planning, control, and motion generation; the development of algorithms for improved estimation of human and humanoid system parameters; and the use of human movement understanding in robotics applications including human-robot interaction and rehabilitation.

The anthropomorphic body form is a complex articulated system of links/limbs and joints, simultaneously redundant and underactuated, and capable of a wide range of sophisticated movement. The human body and its movement have long been a topic of study in physiology, anatomy, biomechanics, and neuroscience and have served as inspiration for humanoid robot design and control. This survey paper reviews the literature on robotics research using anthropomorphic design principles as an inspiration, at both the design and control levels. Next, anthropomorphic body modeling, motion analysis, and synthesis techniques are overviewed. Finally, key applications arising at the intersection of robotics and human movement science are introduced. The survey ends with a discussion of open research questions and directions for future work.

In this paper we present a real-time method for identification of the dynamic parameters of a manipulator and its load using kinematic measurements and either joint torques or force and moment at the base. The parameters are estimated using the Extended Kalman Filter and constraints are imposed using Sigmoid functions to ensure the parameters remain within their physically feasible ranges, such as links having positive masses and moments of inertia. Identified parameters can be used in model based controllers. The presented approach is validated through simulation and on data collected with the Barret WAM manipulator. Using the estimated parameters instead of ones provided by the manufacturer greatly improves joint torque prediction.

The mass parameters of robots influence performances of model-based control and validation of the simulation results. The mass parameters provided by CAD data are usually rough approximation of the true parameters. Therefore several methods for estimation of those parameters have been proposed. Their precision depends on the used motion, called optimal exciting trajectories. This paper describes a new approach to determine humanoid robot exciting trajectories for mass parameters identification. The method was inspired by the studies done in the field of human mass parameters identification, and it is based on observation of condition numbers of sub-regressor matrices created from the columns of the regressor matrix. The method has been experimentally applied to identify mass parameters of HRP-2 and HRP-4 humanoid robots. The proposed method is able to reconstruct ground reaction forces and force moments more accurately than parameters obtained from CAD data.

Optical motion capture systems, which are used in broad fields of research, are costly; they need large installation space and calibrations. Applying this technology in typical homes and care centers is unrealistic. Low cost motion capture systems such as Microsoft Kinect are based on video, thus privacy issues might arise from their usage. Therefore we propose to use low cost contact force measurement systems to develop rehabilitation and healthcare monitoring tools that can be used widely. Here, we propose a novel algorithm for motion recognition using the feature vector from force data solely obtained during a daily exercise program. We recognized 7 types of movement (Radio Exercises) of 5 candidates (mean age 24, male). The results show that the average recognition rate for each motion has good score (mean: 75%), but leave room for improvement. The results also confirm that there is a dynamic signature in each movement allowing inter-personal recognition. Thus it is possible using inexpensive contact force measurement for motion analysis and motion recognition for applications such as rehabilitation or healthcare.

Reading other's emotions is the key of a successful human-human communication. If we imagine using robots in human environment, the ability of robots to express emotions will help human communicate with the artificial agents. In studies about robots' expressing emotions, conversations, facial expression and simple gestures are mainly used. However, another way to express emotions is necessary to enable the expression in various scenes. This study focused on emotions conveyed by gait and examined the effectiveness of expression of emotions of a humanoid robot during gait. In the experiment, we generated 5 kinds of robot's emotional gait and showed them to the participants. Then participants forecast robot's emotion. This experiment was performed in Japan and in France, to investigate some cultural differences. As a result, this study shows that it is possible to convey emotions by gait for a humanoid robot and also that there is a cultural difference in forecasting emotions.

SWoOZ is an intuitive teleoperation platform using a humanoid robot as a proxy between two humans: a remote user teleoperating the robot and a local user interacting directly with it. NAO (Aldebaran) is the proxy used in this study. The remote user controls its head motion with his own movements (live) while his real voice is transmitted to the local user with an unnoticeable lag time. This paper presents a user study of the platform in the context of a long-distance survey and investigates the possible effect of the remote user's previous experience with robots on the local users' evaluation of the proxy. Although found useful, likable and satisfying by all the local users, only the ones interviewed by the non-naive user find it averagely credible. Results fail to validate an effect of the remote users' previous experience with robots on the local users.

This paper describes the identification of HRP-2 foot's dynamics. It is expected that a humanoid robot will work in the same environment as man. For that purpose, safety of operation is important. Although a simulator is used for confirming safe conditions of operation, an error may arise in a dynamic parameter by the robot of a simulator, and an actual robot. In this paper, it identified about the viscoelasticity of the sole bush for impact absorption at the time of the walk of humanoid robot HRP-2. We used some simple active motions and composed these motions. We identified parameter using composed motions. We finally compare the identified parameters using the experimental results and simulator results.

For any rigid robot, a set of 14 standard parameters characterises the dynamics of each of its links and joints. Only a subset of these standard parameters: the base parameters have unique values identified with the Inverse Dynamic Identification Model and linear least squares techniques (IDIM-LS). Moreover, some of the base parameters are poorly identified when their effect on the joint torques is too small. They can be eliminated, leading to a new subset of essential (base) parameters. However, the consistency of the identified values of the base or the essential parameters cannot be guaranteed, regarding to the loss of the positive definiteness of the robot inertia matrix. The past methods proposed to verify the physical consistency of the identified parameters, relies on complicated, time consuming computations and even leads to non-optimal LS parameters. We propose a method that overcomes these drawbacks, calculating the set of optimal LS standard parameters closest to a set of a priori consistent dynamic parameters obtained through CAD data given by the robot manufacturers. This is a straightforward method, which relies on the use of the Singular Value Decomposition (SVD), the Cholesky factorization and the linear least squares techniques. The method is experimentally validated on a Stäubli TX-40, which is a 6 Degrees of Freedom (DoF) industrial robot. This example enlighten a strong result: the essential base parameters, which have significant identified values with respect to their small relative standard deviation, are consistent.

To facilitate human-robot interactions with the user, it is necessary for the robot to identify the interaction partner. We propose the use of a single wearable sensor worn at the center of the user's belt to record the gait when the interaction partner approaches the robot. Based on the data of a single gait cycle recorded with a single inertial measurement unit (IMU), we identify a person by his/her walking style. For identification, we first detect individual strides. We introduce a simple feature that characterizes the individual's asymmetry of gait and classify the individual using a Bayes classifier. To evaluate our approach, we collect motion data from 20 persons; the classification accuracy based on the proposed asymmetry-based feature reaches 99.3%. We further investigate the robustness of our approach against slight variations in the sensor placement, variations in speed, and walking straight versus walking on a curved route.

This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.

The mass parameters of the human body segments are important when studying motion dynamics and the in vivo method to obtain accurate parameters is required in biomechanics studies and for some medical applications. In our previous works, we proposed the method to identify inertial parameters of human body segments in real-time during measurement of motion. However, some obtained parameters are not physically consistent; some masses are negative and inertia tensor matrices are not positive definite. These parameters generate problems in the analysis and the simulation requiring physical consistency. In this paper, we propose the real-time identification method considering physical consistency.

We propose a fast motion controller for a robot which has a flexible arm using the IDCS control scheme. The IDCS control scheme, which corresponds to a numerical computation of the inverse dynamics of the systems, allows stable high performance control even when the system model is known with inaccuracies. We applied in simulation the IDCS controller to the robot arm and compared the performances of the IDCS to the most commonly controller used industrial robots: PID controller to consider the performances of the IDCS. We can get good simulation model of the robot for the IDCS by means of identifying the inertial parameters using generalized coordinates of the baselink, the joint angles and the external forces information.

In current biomechanics approach, the assumptions are commonly used in body-segment parameters and muscle strength parameters due to the difficulty in accessing those subject-specific values. Especially in the rehabilitation and sports science where each subject can easily have quite different anthropometry and muscle condition due to disease, age or training history, it would be important to identify those parameters to take benefits correctly from the recent advances in computational musculoskeletal modeling. In this paper, Mass Distribution Identification to improve the joint torque estimation and Muscle Strength Identification to improve the muscle force estimation were performed combined with previously proposed methods in muscle tension optimization. This first result highlights that the reliable muscle force estimation could be extracted after these identifications. The proposed framework toward subject-specific musculoskeletal modeling would contribute to a patient-oriented computational rehabilitation.

The mass properties are important to control robot dynamics or study human dynamics. In our previous works, we proposed a method to identify inertial parameters of legged mechanisms from base-link dynamics, using generalized coordinates and external forces information. In this paper, we propose an identification method based on floating-base dynamics, when the system has no external force. Inertial parameters can be identified without force measurement, only from motion data. The method has been tested on two examples; a simple chain consisted of two links and the human body dynamics.

Identification results dramatically depend on the excitation properties of the motion used to sample the identification model. Strategies to define persistent exciting trajectories have been developed for manipulator robots with few DOF. However they can not easily be extended to humanoid systems and humans due to the important number of DOF; and empirical knowledge is often used to generate and select persistent exciting motions. In this paper we propose a method to choose persistent exciting motions from an existing dataset in order to optimize both the identification results and the computation time. This method is based on the use of the identification model of legged systems obtained from the base-link equations. Instead of using well-established consideration on the condition number of the regressor matrix, the method uses a decomposition of the regressor into elementary sub-regressors and the computation of the condition number for each. A selection rule is then proposed. The overall method is experimentally tested to identify the human body inertial parameters using a data-set of 40 motions. Comparative results obtained from different combinations of motions are given.

Mass parameters of the human body segments are mandatory when studying motion dynamics. In orthopedics, biomechanics and rehabilitation they are of crucial importance. Inaccuracies their value generate errors in the motion analysis, misleading the interpretation of results. No systematic method to estimate them has been proposed so far. Rather, parameters are scaled from generic tables or estimated with methods inappropriate for in-patient care. Based on our previous works, we propose a real-time software and its interface that allow to estimate the whole-body segment parameters, and to visualize the progresses of the completion of the identification. The visualization is used as a visual feedback to optimize the excitation and thus the identification results. The method is experimentally tested and obtained results are discussed.

When simulating and controlling robot dynamics it is necessary to know the inertial parameters and the joint dynamics accurately. As these parameters are usually not provided by manufacturers, identification is then an essential step in robotics. In addition with the up coming wide-spreading of humanoid robots in the society the identification of humanoid dynamics has became mandatory to insure safety. This paper proposes a method to estimate humanoid robots inertial parameters using a minimal set of sensors. Only joint angles and external forces information are required. Simulations have provided exciting trajectories that are reproduced on a small-size humanoid robot. Experimental results are given.

We present a solution to estimate in-vivo the joint dynamics of the human limbs during passive movements. The method is based on well-known modelling and approach used in Robotics that allow simultaneous multi-joint estimation. The modelling of the human body and the human joint as well as the method are described. The experimental set-up based on the use of an optical motion capture system is detailed. Three types of movements are recorded and used to perform the identification. We concluded that designed movements and movements from clinical diagnosis of neuromuscular diseases are good to perform the identification; however swing of the arms during normal walk does not provide enough excitation to obtain consistent results.

Human upper limb joints dynamics is very important in the fields of humanoid robotics, medical robotics as well as medical research. To make human-like passive movements of the arms when walking humanoid robot arms must have similar dynamics to the human arms, even more if this arm is to be used as a prosthesis. Moreover medical diagnosis of muscle or neuro-motor diseases are based on a visual qualitative estimation of joint passive stiffness. There is a pressing need in human body dynamics characterization and especially in subject specific characterization. In this paper a solution to estimate in-vivo the passive dynamic of the arm joint is proposed. It is based on the use of the musculo-skeletal description of the human body and its kinematics computation. The linear passive joint dynamics: stiffness, viscosity and friction, is then estimated with least squares method. Acquisition of movements both designed for estimation or from medical diagnosis check-up, are achieved with motion capture studio only (no pain, no distress on subject). Experimental results for three valid subject are given

This paper deals with the problem of dynamic modeling and identification of passenger cars. It presents a new method that is based on robotics techniques for modeling and description of tree-structured multibody systems. This method enables us to systematically obtain the dynamic identification model, which is linear with respect to the dynamic parameters. The estimation of the parameters is carried out using a weighted least squares method. The identification is tested using vehicle dynamics simulation software used by the car manufacturer PSA Peugeot-CitroËn in order to define a set of trajectories with good excitation properties and to determine the number of degrees of freedom of the model. The method has then been used to estimate the dynamic parameters of an experimental Peugeot 406, which is equipped with different position, velocity, and force sensors.

This paper presents a remote handling force feedback coupling for micromanipulation systems. In the literature, the most generally used coupling mode is 'force position'. This kind of control scheme is not portable and instability is an often occurring problem. The coupling scheme proposed in this paper is based on the passivity considerations on the teleoperated systems. It is independent of the used haptic interface and the manipulator and unconditionally stable regarding scaling ratios. It is experimented using the LRP's (Laboratoire de Robotique de Paris) micromanipulator, which is based on AFM architecture and uses the adhesion forces for pickup and release tasks. A comparison between the force position coupling and proposed coupling is presented. Experimental results show the good performances in terms of stability.