Robot Behavior Research Articles

Adaptive humanoid robot behaviour in a serious game scenario through reinforcement learning

ABSTRACT The study presents an adaptive technique that enables a humanoid robot to select appropriate actions to maintain the engagement level of users while they play a serious game for cognitive training. The goal is to design and develop an adaptation strategy for changing the robot's behaviour based on Reinforcement Learning (RL) to encourage the user to remain engaged. Initially, we trained the algorithm in a simulated environment before moving on to a real user experiment. Thus, we first design, develop, and validate the RL strategy in a simulated environment. Subsequently, we integrate the trained policy into the robotic system, allowing it to select the best actions based on the detected user state during real user test. The RL algorithm was designed and implemented to determine an effective adaptation strategy for the robot's actions, encompassing verbal and non-verbal interactions. The proposed solution was first trained in a simulated environment and then tested with 28 users in a mixed-method design study.

Emergence of flocking behaviors transferring previously evolved alignment robot controllers

Flocking is a crucial collective behavior in swarm robotics. Reynolds introduced the boids model as a means to imitate flocking behaviors in artificial agents. This model relies on three fundamental local rules: separation, cohesion, and alignment. This paper examines the development of flocking behaviors only through the evolution of the alignment rule. Initially, we employ a genetic algorithm to develop the alignment behavior inside a group of stationary robots. The advanced alignment robot controller is a continuous-time recurrent neural network (CTRNN). Afterwards, we include the developed controller into a three-layered subsumption architecture in order to accomplish flocking behavior. Aside from the advanced alignment behavior, the architecture also incorporates a rudimentary manually designed obstacle avoidance behavior and a subroutine for moving forward. The initial experiment centers on the progression of alignment among the robots. Advanced communication techniques result in a scalable and precise alignment, where both the message content and its related context are very pertinent. The second experiment investigates the development of flocking behavior. The results indicate that the suggested subsumption architecture is capable of achieving efficient flocking behaviors. In addition, the robot swarm has the ability to navigate around barriers and continue to exhibit flocking behavior once the impediments have been bypassed. Our research indicates that the formation of a cohesive group can occur by implementing a single developed rule, complemented with well designed actions for avoiding obstacles and navigating the environment.

Adaptive-constrained admittance control for physical human–robot interaction

In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments.

Meet the Motivational Robot That Predicts Your Future Feelings

This study explores the potential benefits of robots having the capability to anticipate people’s mental states in an exercise context. We designed 80 utterances for a robot with associated gestures that exhibit a range of emotional characteristics and then performed a 23-person data collection to investigate the effects of these robot behaviors on human mental states during exercise. The results of cluster analysis revealed that (1) utterances with similar meanings had the same effect and (2) the effects of a certain cluster on different people depend on their emotional state. On the basis of these findings, we proposed a robotic system that anticipates the effect of utterances on the individual’s future mental state, thereby choosing utterances that can positively impact the individual. This system incorporates three main features: (1) associating the relevant events detected by sensors with a user’s emotional state; (2) anticipating the effects of robot behavior on the user’s future mental state to choose the next behavior that maximizes the anticipated gain; and (3) determining appropriate times to provide coaching feedback, using predefined rules in the motion module for timing decisions. To evaluate the proposed system’s overall performance comprehensively, we compare robots equipped with the system’s unique features to those lacking these features. We design the baseline condition that lacks these unique features, opting for periodic random selection of utterances for interaction based on the current context. We conducted a 21-person experiment to evaluate the system’s performance. We found that participants perceived the robot to have a good understanding of their mental states and that they enjoyed the exercises more and put in more effort due to the robot’s encouragement.

Sensual Environmental Robots: Entanglements of Speculative Realist Ideas with Design Theory and Practice

Abstract In response to this issue’s theme of Can robots be sensual? two propositions are discussed from a design researcher’s perspective. Four devices across two speculative projects Habitat Robots and Soil Protector Robots are presented. Speculative Realist ideas provide reasoning for design approaches to metaphorise sensed environmental data into multi-sensorial performances that the devices embody. Facilitated through the projects are philosophy of design concerns, such as asymmetrical relations, the nature of data, and language about the devices prefiguring sensorial expectations. The performative behaviours of these multi-sensorial robots evoke a sense of things by removing the truthiness of data and thereby emancipating language from privilege and expertise, subverting expectations of technology, and notions of urban Nature. Through Harman’s Quadruple Object, we speculate on asymmetricality within object relations, including between the devices and the practices of design itself. Thereby, allowing ontological design implications to be reviewed – where acts of designing in turn design us; design processes are not only entwined prefiguratively, but are companion output artefacts of these technological ecology interfaces. The devices bring together and physicalise questions of contextual boundaries and politics between humans, technology, and Nature.

EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection.

When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. This paper proposes an approach to help developers catch these errors as soon as we have some context (typically at pre-launch time) with minimal additional efforts. We use embedded domain-specific language (DSL) techniques to enforce early checks. We describe design patterns suitable for robot programming and show how to use these design patterns for DSL embedding in Python, using two case studies on an open-source robot skill platform SkiROS2, designed for the composition of robot skills. These two case studies help us understand how to use DSL embedding on two abstraction levels: the high-level skill description that focuses on what the robot can do and under what circumstances and the lower-level decision-making and execution flow of tasks. Using our DSL EzSkiROS, we show how our design patterns enable robotics software platforms to detect bugs in the high-level contracts between the robot's capabilities and the robot's understanding of the world. We also apply the same techniques to detect bugs in the lower-level implementation code, such as writing behavior trees (BTs), to control the robot's behavior based on its capabilities. We perform consistency checks during the code deployment phase, significantly earlier than the typical runtime checks. This enhances the overall safety by identifying potential issues with the skill execution before they can impact robot behavior. An initial study with SkiROS2 developers shows that our DSL-based approach is useful for finding bugs early and thus improving the maintainability of the code.

Contact Dynamic Behaviors of Magnetic Hydrogel Soft Robots.

Magnetic hydrogel soft robots have shown great potential in various fields. However, their contact dynamic behaviors are complex, considering stick-slip motion at the contact interface, and lack accurate computational models to analyze them. This paper improves the numerical computational method for hydrogel materials with magneto-mechanical coupling effect, analyses the inchworm-like contact motion of the biomimetic bipedal magnetic hydrogel soft robot, and designs and optimizes the robot's structure. In the constitutive model, a correction factor representing the influence of the direction of magnetic flux density on the domain density has been introduced. The magnetic part of the Helmholtz free energy has been redefined as the magnetic potential energy, which can be used to explain the phenomenon that the material will still deform when the magnetic flux density is parallel to the external magnetic field. The accuracy of the simulation is verified by comparing numerical solutions with experimental results for a magnetic hydrogel cantilever beam. Furthermore, employing the present methods, the locomotion of a magnetic hydrogel soft robot modeled after the inchworm's gait is simulated, and the influence of the coefficient of friction on its movement is discussed. The numerical results clearly display the control effect of the external magnetic field on the robot's motion.

Behavior of a mobile robot with symmetrical structure of mecanium wheels when movement along a curvolinear trajectory

The article presents a comparative analysis of two variants of symmetrical placement of omnidirectional mecanum wheels in a mobile robot. The modeling of the mobile robot behavior with two symmetrical variants of mecanum wheel arrangement along curvilinear trajectories in the MATLAB Simulink environment is considered. Graphs of the robot center trajectories in the X–Y axes are obtained for two variants of symmetrical mecanum wheel configurations when moving along the trajectories of a lemniscate, ellipse, hypotrochoid, and track. Dependences of the robot position errors in the X–Y axes on time are obtained for two variants of wheel topologies when moving along four trajectories. Dependences of the robot body rotation angle deviation on time are analyzed for two variants of wheel topologies when moving along four curvilinear trajectories. The comparative analysis made it possible to give recommendations on movement along the ellipse and lemniscate, which are characterized by smaller position errors and deviations in the rotation angle compared to more complex curves.

Assessing path accuracy in industrial robots via ballbar technology

Purpose This paper aims to present a methodology for evaluating the path accuracy of industrial robots using the telescoping ballbar measurement technology. The goal is to improve accuracy assessments in precision-driven manufacturing processes. Design/methodology/approach A single telescoping ballbar is used to assess the circle contouring performance of a KUKA KR210 R2700 prime robot. Experiments involve system setup, data collection and analysis in Matlab to derive performance metrics such as radial deviation, circularity and path accuracy error. This study investigates the impact of varying the operational conditions, including speed, payload and robot configuration, on these indexes through statistical analysis, and examines the relationship between joint errors and path deviations. Findings The results indicate that the robot behavior is influenced by the operating conditions, with notable error spikes at joint reversal positions due to factors such as joint backlash and transmission errors. This study evaluates various performance indexes from different standards, ISO 230 and ISO 9283, and identifies key operating parameters influencing each index. The findings suggest effective strategies for error compensation and performance enhancement. Originality/value This paper offers a novel approach to path accuracy verification and error source identification in industrial robots. It proposes methods to rapidly assess the correlation between performance and operating conditions, offering insights for better calibration and control strategies, especially in high-precision tasks.

A Bayesian framework for learning proactive robot behaviour in assistive tasks

Socially assistive robots represent a promising tool in assistive contexts for improving people’s quality of life and well-being through social, emotional, cognitive, and physical support. However, the effectiveness of interactions heavily relies on the robots’ ability to adapt to the needs of the assisted individuals and to offer support proactively, before it is explicitly requested. Previous work has primarily focused on defining the actions the robot should perform, rather than considering when to act and how confident it should be in a given situation. To address this gap, this paper introduces a new data-driven framework that involves a learning pipeline, consisting of two phases, with the ultimate goal of training an algorithm based on Influence Diagrams. The proposed assistance scenario involves a sequential memory game, where the robot autonomously learns what assistance to provide when to intervene, and with what confidence to take control. The results from a user study showed that the proactive behaviour of the robot had a positive impact on the users’ game performance. Users obtained higher scores, made fewer mistakes, and requested less assistance from the robot. The study also highlighted the robot’s ability to provide assistance tailored to users’ specific needs and anticipate their requests.

Development of a Robotic Device that Performs Head Bunting to Relieve User Tension

Inspired by an animal behavior called bunting, in which the animal rubs its head against other objects, including humans, we developed robotic prototypes capable of performing such bunting behaviors. Since physical contact plays an important role in therapeutic interactions between pets and their owners, we hypothesize that robot bunting can have a similar effect on its user. This article reports on the development of such a robot with a flexible neck that can change its stiffness while the robot is rubbing its head against the user. An exploratory study was also conducted with 22 human participants, on whom the developed robot performed head bunting with three different (low/high/variable) stiffness conditions. The results show that the participants’ psychological tension, as measured by the temporary mood scale (TMS), was significantly reduced (p \(\lt\) 0.001) after interacting with the robot. The difference between the three stiffness conditions was not significant in this study. Due to the lack of a control condition, we cannot confirm a clear effect of the stiffness change; however, some participants commented that the stiffness change made the robot's behavior lifelike and relaxing.

Pneumatic coding blocks enable programmability of electronics-free fluidic soft robots.

Decision-making based on environmental cues is a crucial feature of autonomous systems. Embodying this feature in soft robots poses nontrivial challenges on both hardware and software that can undermine the simplicity and autonomy of such devices. Existing pneumatic electronics-free soft robots have so far mostly been approached by using system fluidic circuit architectures analogous to digital electronics. Instead, here we design dedicated pneumatic coding blocks equivalent to If, If...break, and For software control statements, which are based on the analog nature of nonlinear mechanical components. We demonstrate that we can combine these coding blocks into programs to implement sequences and to control an electronics-free autonomous soft gripper that switches between behaviors based on interactions with the environment. As such, our strategy provides an alternative approach to designing complex behavior in soft robotics that is more reminiscent of how functionalities are also encoded in the body of living systems.

Roboethics for everyone – A hands-on teaching module for K-12 and beyond

In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.

Printing Untethered Self-Reconfigurable, Self-Amputating Soft Robots from Recyclable Self-Healing Fibers.

Regarding the challenge of self-reconfiguration and self-amputation of soft robots, existing studies mainly focus on modular soft robots and connection methods between modules. Different from these studies, this study focus on the behavior of individual soft robots from a material perspective. Here, a kind of soft fibers, which consist of hot melt adhesive particles, magnetizable microparticles, and ferroferric oxide microparticles embedded in a thermoplastic polyurethane matrix are proposed. The soft fibers can achieve wireless self-healing and reversible bonding of the fibers by eddy current heating and can be actuated by magnetic fields. Moreover, the soft fibers are recyclable and printable. Building on this material foundation, an integrated material-structure-actuation printing strategy using soft fibers for the design and fabrication of soft robots are reported. The robots printed by this strategy can achieve their untethered motions and wireless self-healing. Soft gripper, soft crawling robot, and soft multi-legged robot, are then fabricated which demonstrates the self-healing, self-reconfigurable, self-amputating, and sustainable performances of soft robots so as to adapt to different environments and tasks. This integrated material-structure-actuation printing strategy using soft fibers is universal, easy to implement, and mass-manufactured, opening a door for sustainable, eco-friendly, untethered, self-reconfigurable, self-amputating soft robots.

Personalization of Robot Behavior Using Approach Based on Model Predictive Control

This paper proposes a novel approach to personalizing robot behavior using Model Predictive Control (MPC). Social humanoid robots, equipped with advanced sensors and human-like capabilities, are increasingly integrated into human environments, necessitating adaptable and intuitive communication interfaces. Our approach enables the design of adaptive interfaces that support fluid, personalized human–robot interactions. In the proposed framework, a user model is applied to predict responses to potential robot actions. Initially, this model represents an average user; however, it is updated as the robot gathers new observations, leading to increasingly personalized decisions. Experiments assessed the performance of five machine-learning algorithms generating user models in a simulated environment, with the Light Gradient Boosting Machine (LGBM) achieving the best results, closely followed by Random Forest (RF). A comparison of the inference time showed that LGBM is more than four times faster than RF. Outlier-removal techniques showed a modest performance improvement over models without outlier removal. Additionally, robot adaptation was tested in the experiments, showing an increase in the average reward over time, although with a relatively high standard deviation. The results suggest that the proposed approach for robot behavior adaptation based on MPC works well, and the recommended algorithm for the user model is LGBM.

A Hybrid Cognitive Architecture to Generate, Control, Plan, and Monitor Behaviors for Interactive Autonomous Robots

AbstractInteractive robots not only need to react in predefined or deterministic scenarios but also learn and adapt in real-time, mirroring cognitive flexibility akin to human intelligence. Achieving this autonomy entails developing cognitive architectures that integrate reactive, deliberative and emergent capabilities. Thus, this paper presents MERLIN2, a hybrid cognitive architecture to generate, control, plan, and monitor behaviors in autonomous robots. This architecture combines reactive, deliberative, and emergent components, aiming to enhance adaptability in dynamic environments and make intelligent real-time decisions, thereby improving autonomy and performance. MERLIN2 comprises a deliberative system, based on a knowledge base and a symbolic planner; and a behavioral system composed of reactive components and several emergent components. It addresses core cognitive aspects like action selection, perception, memory, learning, reasoning, and explainability. MERLIN2 is evaluated in a simulated world and in the real world Carry My Luggage task from the RoboCup@Home. Therefore, the experimentation presented in this article showcases the architecture as a valid solution for autonomous robots.

Tactile Interaction with Social Robots Influences Attitudes and Behaviour

Tactile Interaction with Social Robots Influences Attitudes and Behaviour

Expressing Anger with Robot for Tackling the Onset of Robot Abuse

Service robots in public spaces can sometimes become victims of ‘abuse’, manifesting as persistent blocking of the robot’s path, physical violence such as pushing or pulling, or abusive language. This kind of abuse can be a serious obstacle to the deployment of robots. We studied the possibility of mitigating obstructive behaviour towards the robot, which is known to happen in the early stages of robot abuse, by having the robot express anger-like emotions. We identified and implemented three distinct anger behaviours: furious, quiet and scolding anger. Through an online video survey, we confirmed that all three designed robot behaviours were perceived as expressions of anger. Finally, we ran an in-lab experiment with child participants to study the influence of the robot’s expressions of anger on children’s obstructive behaviours. The results show that ‘furious anger’ was effective in reducing the frequency of obstructions, whereas the other two anger types were not.

Automatic Temperature Parameter Tuning for Reinforcement Learning Using Path Integral Policy Improvement.

In this article, we propose a novel variant of path integral policy improvement with covariance matrix adaptation ( [Formula: see text] - [Formula: see text] ), which is a reinforcement learning (RL) algorithm that aims to optimize a parameterized policy for the continuous behavior of robots. [Formula: see text] - [Formula: see text] has a hyperparameter called the temperature parameter, and its value is critical for performance; however, little research has been conducted on it and the existing method still contains a tunable parameter, which can be critical to performance. Therefore, tuning by trial and error is necessary in the existing method. Moreover, we show that there is a problem setting that cannot be learned by the existing method. The proposed method solves both problems by automatically adjusting the temperature parameter for each update. We confirmed the effectiveness of the proposed method using numerical tests.

Fusion-based extended social force model for reciprocal transformation tasks in bidirectional pedestrian movement

This paper proposes a fusion-based extended social force model specifically designed to simulate collective behavior in bidirectional pedestrian flow. By integrating the social force model with the classic Vicsek model, the research not only examines adjustments in individuals’ desired speeds but also introduces a correction mechanism for alignment effects, aiming to enhance the model’s representation of realistic pedestrian movement. The accuracy of the model was validated through numerical simulations, and key parameters influencing group dynamics were further analyzed. The results demonstrate that the model effectively reduces traffic congestion and improves transportation efficiency, with particularly significant effects observed in smaller groups. The study’s findings contribute meaningfully to the theoretical framework of the social force model in bidirectional pedestrian dynamics and may have potential applications in the design of collaborative robotic swarm behaviors.