Robot Behavior Research Articles

Influence of the Behavior of a Para-Operated Social Robot on the Impression Interlocutors Have of the Operator: an Online Study

Influence of the Behavior of a Para-Operated Social Robot on the Impression Interlocutors Have of the Operator: an Online Study

EchoPT: A Pretrained Transformer Architecture that Predicts 2D In-Air Sonar Images for Mobile Robotics

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.

A modified extended Kalman filter based fusion of auto-correlation data for robot SLAM estimation

Abstract. With the popularization of robot technology, various technological developments related to robots have gradually entered a stage of rapid development. Especially path planning technology, which has been a very important and worthwhile part of robot motion and behavior since the concept of robots emerged. In this technology, various obstacles, environmental changes, and efficiency factors usually need to be considered. Whether in the past, present, or future, robot behavior pursues greater precision, reliability, adaptability to the environment, and even anthropomorphism. In order to achieve these goals, the idea of data fusion has emerged in the development of robot data processing. Therefore, the research direction is to use Kalman filtering for noise filtering and data correction to improve the accuracy of fused data. By using a feasible and strongly correlated data fusion method, combined with an improved extended Kalman filter, various data generated by robots can be fused in multiple dimensions and have correlations, reducing the negative impact of data differences and improving the quality and reliability of data fusion.

The Journey or the Destination: The Impact of Transparency and Goal Attainment on Trust in Human-Robot Teams

As robots gain autonomy, human-robot task delegation can become more goal-oriented; specifying what to do rather than how. This can lead to unexpected robot behaviour. We investigated the effect of transparency and outcome on the perceived trustworthiness of a robot that deviates from the expected manner to reach a delegated goal. Participants (N = 82) engaged in a virtual military mission as a Human-Robot Team using a 2x2 between-subjects design (low vs. high transparency, positive vs. negative outcome). Participants received training on the expected manner to reach the mission’s goal. In the actual mission, the robot deviated from the planned path. We manipulated whether the robot explained its deviation and whether the outcome was better or worse than the original plan. Results showed that transparency contributed to higher and more stable levels of trust, without increasing subjective workload. While the robot’s deviation led to a violation of trust in the low transparency condition, trust remained stable in the high transparency condition, indicating a buffering effect of transparency on trust in case of unexpected behaviour. The impact of outcome on trust was consistent across transparency conditions. Our findings underscore the role of transparency as a tool for fostering human-robot trust.

An empirical evaluation of a formal approach versus ad hoc implementations in robot behavior planning

As autonomous robotic systems integrate into various domains, ensuring their safe operation becomes increasingly crucial. A key challenge is guaranteeing safe decision making for cyber-physical systems, given the inherent complexity and uncertainty of real-world environments.Tools like Gwendolen, vGOAL, and Tumato enable the use of formal methods to provide guarantees for correct and safe decision making. This paper concerns Tumato, a formal planning framework that generates complete behavior from a declarative specification. Tumato ensures safety by avoiding unsafe actions and states while achieving robustness by considering nondeterministic outcomes of actions. While formal methods claim to manage complexity, provide safety guarantees, and ensure robustness, empirical evaluation is necessary to validate these claims.This work presents an empirical study comparing the characteristics of various ad hoc behavior planning implementations (developed by participants with diverse levels of experience in computer science), with implementations using Tumato. We investigate the usability of the different approaches and evaluate i) their effectiveness, ii) the achieved safety (guarantees), iii) their robustness in handling uncertainties, and iv) their adaptability, extensibility, and scalability. To our knowledge, this is the first participant-based empirical study of a formal approach for (safe and robust) autonomous behavior.Our analysis confirms that while ad hoc methods offer some development flexibility, they lack the rigorous safety guarantees provided by formal methods. The study supports the hypothesis that formal methods, as implemented in Tumato, are effective tools for developing safe autonomous systems, particularly in managing complexity and ensuring robust decision making and planning.

Comparative Analysis of Value Iteration and Policy Iteration in Robotic Decision-Making

This study focuses on Markov Decision Processes (MDPs) as a framework for decision-making in robotics. MDPs are crucial for enabling autonomous systems to operate in dynamic environments. MDPs enable the development of optimal strategies that balance immediate and future rewards, making them essential for intelligent robotic behavior. The purpose of this study is to evaluate the performance differences of value iteration and strategy iteration algorithms in different robot tasks. The study highlights the strengths and weaknesses of each algorithm. This comparative analysis uniquely addresses a gap in existing research by evaluating both Value Iteration and Policy Iteration side by side, offering critical insights into their respective performances across diverse robotic tasks. Results indicate that Policy Iteration converges faster and adapts better in complex environments, making it ideal for real-time applications, while Value Iteration is more efficient in smaller state spaces.These findings provide critical insights for selecting algorithms tailored to specific robotic applications, highlighting their role in advancing intelligent robotic systems.

A Bio-Inspired Magnetic Soft Robotic Fish for Efficient Solar-Energy Driven Water Purification.

Solar-driven water evaporation is a promising solution for global water scarcity but is still facing challenges due to its substantial energy requirements. Here, a magnetic soft robotic bionic fish is developed by combining magnetic nanoparticles (Fe3O4), poly(N-isopropylacrylamide), and carboxymethyl chitosan. This bionic fish can release liquid water through hydrophilic/hydrophobic phase transition and dramatically reduce energy consumption. The introduced Fe3O4 nanoparticles endow the bionic fish with magnetic actuation capability, allowing for remote operation and recovery. Additionally, the magnetic actuation process accelerates the water absorption rate of the bionic fish as confirmed by the finite element simulations. The results demonstrate that bionic fish can effectively remove not only organic molecular dyes dissolved in water but also harmful microbes and insoluble microparticles from natural lakes. Moreover, the bionic fish maintains a good purification efficiency even after five recycling cycles. Furthermore, the bionic fish possesses other functions, such as salt purification and salt rejection. Finally, the mechanism of water purification is explained in conjunction with molecular dynamics calculations. This work provides a new approach for efficient solar-energy water purification by phase transition behavior in soft robotics.

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 paper 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.

Classification of Co-Manipulation Modus with Human-Human Teams for Future Application to Human-Robot Systems

Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing \(27 \mathrm{kg}\) without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification were quickly , smoothly and avoiding obstacles . Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team.

Diffusion policy: Visuomotor policy learning via action diffusion

This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details are available (diffusion-policy.cs.columbia.edu).

Optimizing the grasping behavior of a soft robot's gripper

AbstractIn this study, smart composite materials based on 4D printing technology were used to determine the grasping ability of four‐claw gripper of soft robot. First, fused deposition modeling (FDM) is used to prepare polylactic acid (PLA) strips on the surface of a rectangular bamboo substrate to create a single‐claw gripper for a smart composite soft robot, while using stimulus (heat) to deform and recover its original shape. Here, the authors first evaluate the deformation and recovery angle of the single‐claw gripper of the smart composite soft robot, as well as various processing parameters such as heating temperature and time. This study then used FDM to print PLA ribbons on cross‐shaped bamboo sheets to design the four‐claw gripper of soft robot and its gripper technology was actuation type. Then, the four‐claw gripper of soft robot was used to grab the table tennis ball to test its grasping ability. Finally, four processing parameters (width of claw, pitch of PLA ribbon, heating temperature, and heating time) were applied to determine the grasping ability of the four‐claw gripper of soft robot. The optimal processing parameters for the grasping ability of the four‐claw gripper of soft robot were width of claw of 20.2 mm, pitch of PLA ribbon of 1 mm, heating temperature of 200°C, and the heating time of 15 s. The authors also applied the operation window of two processing parameters to discuss the success of the grasping ability of the four‐claw gripper of soft robot. The innovation of this study is the use of PLA/bamboo composite materials as the four‐claw gripper of soft robot and these materials are cheap, easy to obtain, and can be recycled naturally. This study uses a simple thermal stimulus to enable the four‐claw gripper of soft robot to easily grasp irregular objects.Highlights The innovative composite material of polylactic acid and bamboo fabricated by 3D printing. Width of claw and heating temperature are the key factors on grasping ability. Operating window of grasping ability appears on four‐claw gripper of soft robot.

Social robot detection based on user behavioral representation

Social robots aim to facilitate our lives by mimicking our behaviors to deliver information and interact with us. Unfortunately, some social robots are bred for sinister intention, which often spread fake news, even threatening personal and national security. To effectively detect these robots, some existing works utilize basic user attributes for clustering, such as creation time and other information, but ignore the behavioral features of social robots, which are important a priori information for detection, and their absence leads to poor performance. To address this problem, we construct the Social Robot Behavior Detection model, which employs social robots' behavioral habits in disseminating content. Specifically, our method consists of three parts. First, to accurately characterize news, we analyze news features from multiple views, which facilitates representing users' habits of posting news. Second, a novel aggregation strategy is designed for aggregating user features to obtain accurate user representation. Finally, a classifier is used to detect whether user is social robots or not. We conduct analytical experiments on two public datasets, and the results show that our model has better detection performance, which demonstrates that our approach outperforms existing works on social robot detection. Our source code is available at https://github.com/NPURG/SRDB.

Development of a Mathematical Model of a Highly Maneuverable Robot for Simulation of Robots with Different Types of Designs

The article presents the results of developing a mathematical model and simulation modeling of a three-wheeled highly maneuverable mobile robot. A mathematical model of the robot in the state space has been developed. The possibility of using this robot with rotary modules for analyzing and testing control algorithms for robots of various designs has been considered. The relevance of the study is justified by the high costs of creating physical models of robots for experimental verification of control algorithms. The proposed three-wheeled mobile robot with rotary modules, where each wheel is simultaneously both a driving and a steering wheel, allows reducing these costs, while maintaining the ability to simulate part of the real operating conditions. Simplified diagrams of the robot are given, the main parameters and equations characterizing the change in linear and angular velocity are described. Particular attention is paid to modeling various types of robot drives: automotive type and with a differential drive. In the discrete state space, equations describing the dynamics of the robot were presented, which allows them to be used in software products for modeling. The results of simulation modeling obtained in the SimInTech software environment confirm the effectiveness of the proposed model. The analysis of the obtained results showed that the use of a three-wheeled robot with rotary modules allows successfully simulating the behavior of robots with other types of drives and creating conditions close to real ones for testing control algorithms. The proposed model can be used to improve the quality of design and testing of control algorithms in robotics, while reducing the costs of creating physical models of robots, as well as in the educational process.

A survey of communicating robot learning during human-robot interaction

For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior.

An Emotional-based Methodology to Detect Preferences in a Decision-making Process Applied to a Virtual Service Robot

In a multi-objective problem, no single solution optimally satisfies all objectives. Thus, the challenge is to find a balance between conflicting objectives. The decision-making necessarily requires human intervention. The person responsible for selecting the most appropriate solution among all the trade-off solutions is the decision maker (DM). The DM seeks to approach only the solutions that best suit her/his preferences. Since there is plenty of specialized literature showing that emotions play a critical role in decision-making, we aim to incorporate them into the decision-making process. To elicit emotions that can be quantifiable, we propose the Emotional Assessment Method. The method presents a simulation of the objectives to be optimized that represents the consequences of each decision. Using this methodology, the decision maker assesses the emotions evoked by each presented simulation to guide his/her search for solutions that satisfy his/her preferences. As a case study, we aim to identify subjects’ preferences towards robot behaviors. Seventy-two subjects with varying levels of familiarity with robots (divided into two datasets) participated in the experiments. We concluded that the method elicits subjects’ emotions while observing the consequences of the robot’s performance. Also, we found out that it is possible to identify subjects’ preferences based on both the context and the emotions to select the robot’s behavior.

A Survey of Robot Intelligence with Large Language Models

Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks. These abilities of LLMs are promising in robotics. In general, traditional supervised learning-based robot intelligence systems have a significant lack of adaptability to dynamically changing environments. However, LLMs help a robot intelligence system to improve its generalization ability in dynamic and complex real-world environments. Indeed, findings from ongoing robotics studies indicate that LLMs can significantly improve robots’ behavior planning and execution capabilities. Additionally, vision-language models (VLMs), trained on extensive visual and linguistic data for the vision question answering (VQA) problem, excel at integrating computer vision with natural language processing. VLMs can comprehend visual contexts and execute actions through natural language. They also provide descriptions of scenes in natural language. Several studies have explored the enhancement of robot intelligence using multimodal data, including object recognition and description by VLMs, along with the execution of language-driven commands integrated with visual information. This review paper thoroughly investigates how foundation models such as LLMs and VLMs have been employed to boost robot intelligence. For clarity, the research areas are categorized into five topics: reward design in reinforcement learning, low-level control, high-level planning, manipulation, and scene understanding. This review also summarizes studies that show how foundation models, such as the Eureka model for automating reward function design in reinforcement learning, RT-2 for integrating visual data, language, and robot actions in vision-language-action models, and AutoRT for generating feasible tasks and executing robot behavior policies via LLMs, have improved robot intelligence.

A triboelectric flexible pressure sensors based on synergistic enhancement of material and structure for robot behavior perception

A triboelectric flexible pressure sensors based on synergistic enhancement of material and structure for robot behavior perception

Subtractive manufacturing of composite materials with robotic manipulators: a comprehensive review

AbstractRobotic manipulators play an innovative role as a new method for high-precision, large-scale manufacturing of composite components. However, machining composite materials with these systems presents unique challenges. Unlike traditional monolithic materials, composites exhibit complex behaviour and inconsistent results during machining. Additionally, robotic manipulator as a machine tool often associates with stiffness and vibration issues which adds another layer of complexity to this approach. By employing a comprehensive analysis and a combination of quantitative and qualitative review methodology, this review paper aims to survey diverse properties of composite materials by different categories and their interaction with machining processes. Subsequently, a survey of manufacturing techniques for composite machining following with a review in various modeling practices to capture material machining behaviour under a systematic framework is presented. Thereafter, the reviewed literature examines the errors inherent in robotic systems, alongside ongoing research efforts in modeling to characterise robot behaviour and enhance its performance. Afterward, the paper explores the application of data-driven modelling methods, with a primary focus on digital twins, in enabling real-time monitoring and process optimisation. Finally, this paper aims to identify the gap in this field and suggests the potential routes for future research and application as well as their challenges.

Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements.

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

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.