Manipulability, force, and compliance analysis for planar continuum manipulators.

Continuum manipulators can exhibit behaviors similar to snakes, elephant's trunks, and octopus tentacles, which possess incredible dexterity and intrinsic compliance for safe interaction with unstructured environments. However, the lack of enough stiffness limits their scope and application. In this paper, we introduce four different structures of continuum manipulators, including a simple structure with three elastic actuated rods, a structure with a passive rigid kinematic chain, a structure with a hyperelastic notochord and a segmental structure inspired from the spine without articular structure. This paper presents an experimental comparison of four continuum manipulators and aims to explore approaches to enhance the stiffness of a continuum manipulator and maintain the simple constant curvature kinematics as far as possible simultaneously. Results of this paper contribute to guiding the design of a continuum manipulator for achieving desired stiffness properties.

Human activity recognition is crucial for intuitive cooperation between humans and robots. We present an approach for activity recognition for applications in the context of human-robot interaction in industrial settings. The approach is based on spatial and temporal features derived from skeletal data of human workers performing assembly tasks. These features were used to train a machine learning framework, which classifies discrete time frames with Random Forests and subsequently models temporal dependencies between the resulting states with a Hidden Markov Model. We considered the following three groups of activities: Movement, Gestures, and Object handling. A dataset has been collected which is comprised of 24 recordings of several human workers performing such activities in a human-robot interaction environment, as typically seen at small and medium-sized enterprises. The evaluation shows that the approach achieves a recognition accuracy of up to 88% for some activities and an average accuracy of 73%.

Enhancing Humans Trust in Robots through Explanations

Development of robotics and growing use of robots in many areas of the economy and social life require research which will identify problems related to the use of modern technologies and the development of human-robot interaction (HRI, Human-Robot Interaction). Robotics, autonomous systems and artificial intelligence also create a number of new legal and ethical challenges. Research in this area has been expanding in recent years, but there are still issues that require in-depth scientific exploration. The objective of this paper is to systematize knowledge about interactions between people and robots, with particular emphasis on interactions in the work environment, and to identify research gaps in the analysed problem area. The following research questions were formulated: 1) What determines people's attitudes towards robots? 2) What are the key challenges in human-robot communication? 3) What ethical challenges determine human-robot interaction? In the studies the the method of narrative literature review has been used. The latest scientific publications on the researched issue were analysed. The results of the study indicate that the main factors which determine people's attitudes towards robots are apart from demographic variables and experiences gained by people from contacts with robots, the perceived usefulness of using robots, the image of robots created in the mass media and religiousness. The key challenges in human-robot interaction apart from effective communication is the reflection of emotions. The research results enable the identification of further directions of scientific research on human-robot interaction. They can also enrich practitioner’s perspective in the studied area.

Physical interaction of robots with their environment is a challenging problem because of the exchanged forces. Hybrid position/force control schemes often exhibit problems during the contact phase, whereas impedance control appears to be more simple and reliable, especially when impedance is shaped to be energetically passive. Even if recent technologies enable shaping the impedance of a robot, how best to plan impedance parameters for task execution remains an open question. In this chapter, we present an optimization-based approach to plan not only the robot motion but also its desired end-effector mechanical impedance. We show how our methodology is able to take into account the transition from free motion to a contact condition, typical of physical interaction tasks. Results are presented for planar and three-dimensional open-chain manipulator arms. The compositionality of mechanical impedance is exploited to deal with kinematic redundancy and multi-arm manipulation.

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field.

Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing, we directly prescribe the filament's natural curvature-twist field κ(s) through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature (TNI), the helically aligned liquid crystal elastomer regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active-passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

Social navigation framework for assistive robots in human inhabited unknown environments

Natural human–robot interaction (HRI) in complex and unpredictable environments is important with many potential applications. While vision-based HRI has been thoroughly investigated, robot hearing and audio-based HRI are emerging research topics in robotics. In typical real-world scenarios, humans are at some distance from the robot and, hence, the sensory (microphone) data are strongly impaired by background noise, reverberations and competing auditory sources. In this context, the detection and localization of speakers plays a key role that enables several tasks, such as improving the signal-to-noise ratio for speech recognition, speaker recognition, speaker tracking, etc. In this paper we address the problem of how to detect and localize people that are both seen and heard. We introduce a hybrid deterministic/probabilistic model. The deterministic component allows us to map 3D visual data onto a 1D auditory space. The probabilistic component of the model enables the visual features to guide the grouping of the auditory features in order to form audiovisual (AV) objects. The proposed model and the associated algorithms are implemented in real-time (17 FPS) using a stereoscopic camera pair and two microphones embedded into the head of the humanoid robot NAO. We perform experiments with (i) synthetic data, (ii) publicly available data gathered with an audiovisual robotic head, and (iii) data acquired using the NAO robot. The results validate the approach and are an encouragement to investigate how vision and hearing could be further combined for robust HRI.

This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human-robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system's efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor's intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system's performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

PurposeThe purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.Design/methodology/approachThe paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.FindingsThis paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.Originality/valueThe theoretical analysis of this paper provides basic reference for the structural design of tracked robots.

To facilitate human–robot interaction (HRI) tasks in real-world scenarios, service robots must adapt to dynamic environments and understand the required tasks while effectively communicating with humans. To accomplish HRI in practice, we propose a novel indoor dynamic map, task understanding system, and response generation system. The indoor dynamic map optimizes robot behavior by managing an occupancy grid map and dynamic information, such as furniture and humans, in separate layers. The task understanding system targets tasks that require multiple actions, such as serving ordered items. Task representations that predefine the flow of necessary actions are applied to achieve highly accurate understanding. The response generation system is executed in parallel with task understanding to facilitate smooth HRI by informing humans of the subsequent actions of the robot. In this study, we focused on waiter duties in a restaurant setting as a representative application of HRI in a dynamic environment. We developed an HRI system that could perform tasks such as serving food and cleaning up while communicating with customers. In experiments conducted in a simulated restaurant environment, the proposed HRI system successfully communicated with customers and served ordered food with 90% accuracy. In a questionnaire administered after the experiment, the HRI system of the robot received 4.2 points out of 5. These outcomes indicated the effectiveness of the proposed method and HRI system in executing waiter tasks in real-world environments.

ABSTRACTThis paper presents a parameter estimation and an adaptive sensitivity amplification control (ASAC) for a cable driven lower‐limb exoskeleton (CDLEX) to guarantee the tracking performance and enhance comfort in human‐robot interaction (HRI). The cable‐sheath actuator characterized by remote actuation capabilities and simplicity is applied to the lower‐limb exoskeleton to establish a nonlinear integrated model. Aiming to identify the unknown parameters in this model, a novel adaptive parameter estimation framework driven by the extracted error information is proposed to improve the estimation veracity and convergence rate over to the traditional method. Moreover, a sensitivity amplification control (SAC) is adopted to maximize the sensitivity of the closed‐loop system to external human‐robot interaction (HRI) force/torque, where the stability and robustness are all analyzed. The proposed SAC dose not require the direct measurement of HRI in the SAC scheme. Therefore, it is possible to avoid installing the force/torque sensors between the human and the exoskeleton. To account for uncertainties in the interaction environment, such as the suddenly changing walking trajectories and individual gaits, the Q‐learning algorithm is employed to realize online parameter tuning of SAC. Simulations and practical experiments are provided to illustrate the effectiveness of the proposed strategies.

Robots are being used in the operating room to aid in surgery, prompting changes to workflow and adaptive behavior by the users. This case study presents a methodology for examining human-robot team interaction in a complex environment, along with the results of its application in a study of the effects of experience and workplace culture, for human-robot team interaction in the operating room. The analysis of verbal and non-verbal events in robotic surgery in two different surgical teams (one in the US and one in France) revealed differences in workflow, timeline, roles, and communication patterns as a function of experience and workplace culture. Longer preparation times and more verbal exchanges related to uncertainty in use of the robotic equipment were found for the French team, who also happened to be less experienced. This study offers an effective method for studying human-robot team interaction and has implications for the future design and training of teamwork with robotic systems in other complex work environments.

Audio-visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi-stream Dynamic Bayesian Network and coupled HMM are widely used for audio-visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN) to perform unsupervised extraction of spatial-temporal multimodal features from Tibetan audio-visual speech data and build an accurate audio-visual speech recognition model under a no frame-independency assumption. The experiment results on Tibetan speech data from some real-world environments showed the proposed DDBN outperforms the state-of-art methods in word recognition accuracy.