Challenges In Human-robot Interaction Research Articles

Human-robot collaborative task planning using anticipatory brain responses.

Human-robot interaction (HRI) describes scenarios in which both human and robot work as partners, sharing the same environment or complementing each other on a joint task. HRI is characterized by the need for high adaptability and flexibility of robotic systems toward their human interaction partners. One of the major challenges in HRI is task planning with dynamic subtask assignment, which is particularly challenging when subtask choices of the human are not readily accessible by the robot. In the present work, we explore the feasibility of using electroencephalogram (EEG) based neuro-cognitive measures for online robot learning of dynamic subtask assignment. To this end, we demonstrate in an experimental human subject study, featuring a joint HRI task with a UR10 robotic manipulator, the presence of EEG measures indicative of a human partner anticipating a takeover situation from human to robot or vice-versa. The present work further proposes a reinforcement learning based algorithm employing these measures as a neuronal feedback signal from the human to the robot for dynamic learning of subtask-assignment. The efficacy of this algorithm is validated in a simulation-based study. The simulation results reveal that even with relatively low decoding accuracies, successful robot learning of subtask-assignment is feasible, with around 80% choice accuracy among four subtasks within 17 minutes of collaboration. The simulation results further reveal that scalability to more subtasks is feasible and mainly accompanied with longer robot learning times. These findings demonstrate the usability of EEG-based neuro-cognitive measures to mediate the complex and largely unsolved problem of human-robot collaborative task planning.

Anthropomorphic Dual-Arm Coordinated Control for a Single-Port Surgical Robot Based on Dual-Step Optimization

Effective teleoperation of the small-scale and highly-integrated robots for single-port surgery (SPS) imposes unique control and human-robot interaction challenges. Traditional isometric teleoperation schemes mainly focus on end-to-end trajectory mapping, which is problematic when applied to SPS robotic control, especially for dual-arm coordinated operation. Inspired by the human arm configuration in boxing maneuvers, an optimized anthropomorphic coordinated control strategy based on a dual-step optimization approach is proposed. Theoretical derivation and solvability of the problem are addressed, and the effectiveness of the method is further demonstrated in detailed simulation and in-vitro experiments. The proposed control strategy has been shown to perform dexterous SPS bimanual manipulation more effectively, involving less instrument-interference and is free from singularities, thereby improving the safety and efficiency of SPS operations.

A Cloud-based Robot System for Long-term Interaction: Principles, Implementation, Lessons Learned

Making the transition to long-term interaction with social-robot systems has been identified as one of the main challenges in human-robot interaction. This article identifies four design principles to address this challenge and applies them in a real-world implementation: cloud-based robot control, a modular design, one common knowledge base for all applications, and hybrid artificial intelligence for decision making and reasoning. The control architecture for this robot includes a common Knowledge-base (ontologies), Data-base, “Hybrid Artificial Brain” (dialogue manager, action selection and explainable AI), Activities Centre (Timeline, Quiz, Break and Sort, Memory, Tip of the Day, \( \ldots \) ), Embodied Conversational Agent (ECA, i.e., robot and avatar), and Dashboards (for authoring and monitoring the interaction). Further, the ECA is integrated with an expandable set of (mobile) health applications. The resulting system is a Personal Assistant for a healthy Lifestyle (PAL), which supports diabetic children with self-management and educates them on health-related issues (48 children, aged 6–14, recruited via hospitals in the Netherlands and in Italy). It is capable of autonomous interaction “in the wild” for prolonged periods of time without the need for a “Wizard-of-Oz” (up until 6 months online). PAL is an exemplary system that provides personalised, stable and diverse, long-term human-robot interaction.

A Novel Cognitive Approach for Measuring the Trust in Robots

One of the major challenges in human-robot interaction is to determine the trustworthiness of the robot. In order to enhance and augment the human capabilities by establishing a human robot partnership, it is important to evaluate the reliability and dependability of the robots for the specific tasks. The trust relationship between the human and robot becomes critical especially in the cases where there is strong cohesion between humans and robots. In this article, a cognition based-trust model has been developed which measures the trust and other related cognitive parameters of the robot. This trust model has been applied on a customized robot which performs path planning tasks using three different algorithms. The simulation of the model has been done to evaluate the trust of the robot for the three algorithms. The results show that with each learning cycle of each method, the trust of the robot increases. An empirical evaluation has also been done to validate the model.

Blossom

Blossom is an open-source social robotics platform responding to three challenges in human-robot interaction (HRI) research: (1) Designing, manufacturing, and programming social robots requires a high level of technical knowledge; (2) social robot designs are fixed in appearance and movement capabilities, making them hard to adapt to a specific application; and (3) the use of rigid mechanisms and hard outer shells limits the robots’ expressive capabilities. Addressing these challenges, Blossom aims at three design objectives: accessibility, flexibility, and expressiveness. The robot’s mechanism can be quickly assembled and partially extended by end-users. Blossom’s appearance is open-ended through handcrafted fabric exteriors created and customized by users. Smooth organic movements are achieved with tensile mechanisms, elastic components, and a soft exterior cover attached loosely to the body. Blossom’s smartphone-based gesture generation requires neither programming nor character animation experience, allowing lay users to create their own behaviors. All elements in the design were conceived with a low barrier-of-entry in mind. The result is an accessible and customizable social robot for researchers. This article details the implementation of Blossom’s design and demonstrates the platform’s potential through four field deployment case studies.

Improved Emotion Recognition With a Novel Speaker-Independent Feature

Emotion recognition is one of the latest challenges in human-robot interaction. This paper describes the realization of emotional interaction for a Thinking Robot, focusing on speech emotion recognition. In general, speaker-independent systems show a lower accuracy rate compared with speaker-dependent systems, as emotional feature values depend on the speaker and their gender. However, speaker-independent systems are required for commercial applications. In this paper, a novel speaker-independent feature, the ratio of a spectral flatness measure to a spectral center (RSS), with a small variation in speakers when constructing a speaker-independent system is proposed. Gender and emotion are hierarchically classified by using the proposed feature (RSS), pitch, energy, and the mel frequency cepstral coefficients. An average recognition rate of 57.2% (plusmn 5.7%) at a 90% confidence interval is achieved with the proposed system in the speaker-independent mode.

Human-Robot Interaction II: The Impact of Human Factors on Unmanned Systems

The sophistication of robotics systems has increased the utility of unmanned systems for complex military operations. The increasing capability and functionality of current and future robotics requires comparable enhancements to robotics interfaces and displays for tasking and controlling unmanned systems. The symposium participants identified key issues and challenges in human-robot interaction with respect to situation awareness (SA), perception and performance, and workload. The purpose of the symposium is to discuss current research findings and how these findings can be applied. The authors and audience will discuss the challenges of conducting human-robot interaction (HRI) research and the lessons learned for the design of human-robotic interactions and interfaces.

Up from the Rubble: Lessons Learned about HRI from Search and Rescue

The Center for Robot-Assisted Search and Rescue has collected data at three responses (World Trade Center, Hurricane Charley, and the La Conchita mudslide) and nine high fidelity field exercises. Our results can be distilled into four lessons. First, building situation awareness, not autonomous navigation, is the major bottleneck in robot autonomy. Most of the robotics literature assumes a single operator single robot (SOSR), while our work shows that two operators working together are nine times more likely to find a victim. Second, human-robot interaction should not be thought of how to control the robot but rather how a team of experts can exploit the robot as an active information source. The third lesson is that team members use shared visual information to build shared mental models and facilitate team coordination. This suggests that high bandwidth, reliable communications will be necessary for effective teamwork. Fourth, victims and rescuers in close proximity to the robots respond to the robot socially. We conclude with observations about the general challenges in human-robot interaction.