Methodology and themes of human-robot interaction: a growing research field

Original article can be found at: http://www.intechweb.org/journal.php?id=3 Distributed under the Creative Commons Attribution License. Users are free to read, print, download and use the content or part of it so long as the original author(s) and source are correctly credited.This article discusses challenges of Human-Robot Interaction, which is a highly inter- and multidisciplinary area. Themes that are important in current research in this lively and growing field are identified and selected work relevant to these themes is discussed.Peer reviewe

Interactive spaces for children: gesture elicitation for controlling ground mini-robots

[EN] Interactive spaces for education are emerging as a mechanism for fostering children's natural ways of learning by means of play and exploration in physical spaces. The advanced interactive modalities and devices for such environments need to be both motivating and intuitive for children. Among the wide variety of interactive mechanisms, robots have been a popular research topic in the context of educational tools due to their attractiveness for children. However, few studies have focused on how children would naturally interact and explore interactive environments with robots. While there is abundant research on full-body interaction and intuitive manipulation of robots by adults, no similar research has been done with children. This paper therefore describes a gesture elicitation study that identified the preferred gestures and body language communication used by children to control ground robots. The results of the elicitation study were used to define a gestural language that covers the different preferences of the gestures by age group and gender, with a good acceptance rate in the 6-12 age range. The study also revealed interactive spaces with robots using body gestures as motivating and promising scenarios for collaborative or remote learning activities.This work is funded by the European Development Regional Fund (EDRF-FEDER) and supported by the Spanish MINECO (TIN2014-60077-R). The work of Patricia Pons is supported by a national grant from the Spanish MECD (FPU13/03831). Special thanks are due to the children and teachers of the Col-legi Public Vicente Gaos for their valuable collaboration and dedication.Pons Tomás, P.; Jaén Martínez, FJ. (2020). Interactive spaces for children: gesture elicitation for controlling ground mini-robots. 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Analysis of flexibility in humanoid robot structure design to attain human-like motions / Hanafiah Yussof … [et al.]

This paper presents analyses of flexibility in humanoid robot structure design focusing on design parameters of degree of freedoms and joint angle range characteristic to identify elements that provide flexibility for humanoid robots to attain human-like motion. Description and correlation of physical structure flexibility between human and humanoid robot to perform motion is presented to clarify the elements. This analysis utilized the joint structure design, configuration of degree of freedoms and joint rotation range of a 21-dof humanoid robot Bonten-Maru II. Experiments utilizing this robot were conducted, with results indicates effective design parameters to attain flexibility in human-like motion

What is the Teacher's Role in Robot Programming by Demonstration? - Toward Benchmarks for Improved Learning

Robot programming by demonstration (RPD) covers methods by which a robot learns new skills through human guidance. We present an interactive, multimodal RPD framework using active teaching methods that places the human teacher in the robot's learning loop. Two experiments are presented in which observational learning is first used to demonstrate a manipulation skill to a HOAP-3 humanoid robot by using motion sensors attached to the teacher's body. Then, putting the robot through the motion, the teacher incrementally refines the robot's skill by moving its arms manually, providing the appropriate scaffolds to reproduce the action. An incremental teaching scenario is proposed based on insights from various fields addressing developmental, psychological, and social issues related to teaching mechanisms in humans. Based on this analysis, different benchmarks are suggested to evaluate the setup further

Differences in Learning from Complex Versus Simple Visual Interfaces When Operating a Model Excavator

The goal of this study was to test two visual co-robot interfaces (one simple and one more complex) and their effectiveness in teaching a novice participant to operate a complex machine at a later date without assistance. Participants (N = 113) were randomly assigned to one of three groups (one with a basic user interface, one with a more complex guidance interface, and one without an interface) to test the teaching ability of the co-robot in training the user to perform a task with a remote-controlled excavator. Each group was asked to load dirt from a bin into a small model dump truck (in scale with the excavator) with the help of the robot instructor and were asked to return a few days later to complete the task again without the robot instructor. Trials were monitored for completion time and errors and compared to those of an expert operator. The result was that the simple interface was slightly more effective than the more complex version at teaching humans a complicated task. This suggests that novices may learn better and retain more information when given basic feedback (using operant conditioning principles) and less guidance from robot teachers. As robots are increasingly used to help humans learn skills, industries may benefit from simpler guided instructions rather than more complex versions. Such changes in training may result in improved situational awareness and increased safety in the workplace.Psycholog

Mutual reinforcement learning to improve robots as trainers

Recently, collaborative robots have begun to train humans to achieve complex tasks, and the mutual information exchange between them can lead to successful robot-human collaborations. In this thesis we demonstrate the application and effectiveness of a new approach called mutual reinforcement learning (MRL), where both humans and autonomous agents act as reinforcement learners in a skill transfer scenario over continuous communication and feedback. An autonomous agent initially acts as an instructor who can teach a novice human participant complex skills using the MRL strategy. While teaching skills in a physical (block-building) or simulated (Tetris) environment , the expert tries to identify appropriate reward channels preferred by each individual and adapts itself accordingly using an exploration-exploitation strategy. These reward channel preferences can identify important behaviors of the human participants, because they may well exercise the same behaviors in similar situations later. In this way, skill transfer takes place between an expert system and a novice human operator. We divided the subject population into three groups and observed the skill transfer phenomenon, analyzing it with Simpson' s psychometric model. 5-point Likert scales were also used to identify the cognitive models of the human participants. We obtained a shared cognitive model which not only improves human cognition but enhances the robots cognitive strategy to understand the mental model of its human partners while building a successful robot-human collaborative framework

A NEW APPROACH TO ASSESS HIGH LEVEL PLANNING UNDERLYING COGNITIVE-MOTOR PERFORMANCE DURING COMPLEX ACTION SEQUENCES

While much work has examined low-level sensorimotor planning, only limited efforts have studied high-level motor planning processes underlying the cognitive-motor performance of complex action sequences. Such sequences can generally be successfully executed in a flexible manner and typically involve few constraints. In particular, no past study has examined the concurrent changes of high-level motor plans along with those of mental workload and confidence during practice of a novel complex action sequence. To address this gap, first a computational approach providing markers capturing performance dynamics of action sequences during practice had to be developed since past relevant works only employed fairly rough metrics. Such an approach should provide concise performance markers (e.g., distances, scalar) while still capturing accurately the changes of structure of high-level motor plans during the acquisition of novel complex action sequences. Thus, by adapting the Levenshtein distance (LD) and its operators to the motor domain, a computational approach was first proposed to assess in detail action sequences during an imitation practice task executed by various performers (humans, a humanoid robot) and with flexible success criteria. The results revealed that this approach i) could support accurately comparing the high-level plans generated between performers; ii) provides performance markers (LD, insertion operator) able to differentiate optimal (using a minimum of actions) from suboptimal (using more than a minimum of actions but still reaching the task goal) sequences; and iii) gives evidenced that the deletion operator is a marker of action sequence failure. This computational approach was then deployed to examine during practice the concurrent changes in high-level motor plans underlying action sequence execution with modulation of mental workload and an individual’s confidence in performing the task. The results revealed that as individuals practiced, performance improved (reduction of LD, insertion/substitution and movement time) while the level of mental workload and confidence decreased and increased, respectively. Also, by late practice the sequences were still suboptimal while being executed faster, possibly suggesting different dynamics between the generation of high-level motor plans and their execution. Overall, this work complements prior efforts to assess complex action sequences executed by humans and humanoid robots in the context of cognitive-motor practice, and it has the potential to inform not only human cognitive-motor mechanisms, but also human-robots interactions