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Multi-modal Interactive Perception in Human Control of Complex Objects

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Multi-modal Interactive Perception in Human Control of Compl...

IEEE International Conference on robotics and Automation (ICRA)

作者： Rashida Nayeem Salah Bazzi Mohsen Sadeghi Reza Sharif Razavian Dagmar Sternad Department of Electrical and Computer Engineering Biology Institute for Experiential Robotics Northeastern University Boston MA

Tactile sensing has been increasingly utilized in robot control of unknown objects to infer physical properties and optimize manipulation. However, there is limited understanding about the contribution of different sensory modalities during interactive perception in complex interaction both in robots and in humans. This study investigated the effect of visual and haptic information on humans' exploratory interactions with a ‘cup of coffee’, an object with nonlinear internal dynamics. Subjects were instructed to rhythmically transport a virtual cup with a rolling ball inside between two targets at a specified frequency, using a robotic interface. The cup and targets were displayed on a screen, and force feedback from the cup-and-ball dynamics was provided via the robotic manipulandum. Subjects were encouraged to explore and prepare the dynamics by “shaking” the cup-and-ball system to find the best initial conditions prior to the task. Two groups of subjects received the full haptic feedback about the cup-and-ball movement during the task; however, for one group the ball movement was visually occluded. Visual information about the ball movement had two distinctive effects on the performance: it reduced preparation time needed to understand the dynamics and, importantly, it led to simpler, more linear input-output interactions between hand and object. The results highlight how visual and haptic information regarding nonlinear internal dynamics have distinct roles for the interactive perception of complex objects.

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Understanding URDF: A Dataset and Analysis

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arXiv 2023年

作者： Tola, Daniella Corke, Peter The Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Centre for Robotics Brisbane Australia

As the complexity of robot systems increases, it becomes more effective to simulate them before deployment. To do this, a model of the robot's kinematics or dynamics is required, and the most commonly used format is the Unified Robot Description Format (URDF). This article presents, to our knowledge, the first dataset of URDF files from various industrial and research organizations, with metadata describing each robot, its type, manufacturer, and the source of the model. The dataset contains 322 URDF files of which 195 are unique robot models, meaning the excess URDFs are either of a robot that is multiply defined across sources or URDF variants of the same robot. We analyze the files in the dataset, where we, among other things, provide information on how they were generated, which mesh file types are most commonly used, and compare models of multiply defined robots. The intention of this article is to build a foundation of knowledge on URDF and how it is used based on publicly available URDF files. Publishing the dataset, analysis, and the scripts and tools used enables others using, researching or developing URDFs to easily access this data and use it in their own work. © 2023, CC BY.

关键词： Robots

Energy Minimization using Custom-Designed Magnetic-Spring Actuators

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Energy Minimization using Custom-Designed Magnetic-Spring Ac...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yue Yang Fu Ali U. Kilic David J. Braun Department of Electrical and Computer Engineering SyBBURE Searle Undergraduate Research Program Vanderbilt University Advanced Robotics and Control Laboratory Advanced Robotics and Control Laboratory Vanderbilt University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study introduces an innovative actuator that resembles a motor with a non-uniform permanent magnetic field. We have developed a prototype of the actuator by combining a standard motor, characterized by a uniform magnetic field, with a custom rotary magnetic spring exhibiting a non-uniform magnetic field. We have also presented a systematic computational approach to customize the magnetic field to minimize the energy consumption of the actuator when used for a user-defined oscillatory task. Experiments demonstrate that this optimized actuator significantly lowers energy consumption in a typical oscillatory task, such as pick-and-place or oscillatory limb motion during locomotion, compared to conventional motors. Our findings imply that incorporating task-optimized non-uniform permanent magnetic fields into conventional motors and direct-drive actuators could enhance the energy efficiency of robotic systems.

关键词： Actuators Energy consumption Limbs Systematics Prototypes Motors Minimization Magnetic fields Springs Standards

Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration

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Multimodal Dataset from Harsh Sub-Terranean Environment with...

Mediterranean Conference on Control and Automation (MED)

作者： Alexander Kyuroson Niklas Dahlquist Nikolaos Stathoulopoulos Vignesh Kottayam Viswanathan Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Team Luleå University of Technology Luleå

Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from Radio Detection and Rangings (RADARs) which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.

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GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

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arXiv 2024年

作者： Fredriksson, Scott Saradagi, Akshit Nikolakopoulos, George Robotics & AI Team Department of Computer Science Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structuralsemantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

Updating Robot Safety Representations Online from Natural Language Feedback

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arXiv 2024年

作者： Santos, Leonardo Li, Zirui Peters, Lasse Bansal, Somil Bajcsy, Andrea School of Engineering Federal University of Minas Gerais Brazil Electrical and Computer Engineering University of Rochester United States Department of Cognitive Robotics Delft University of Technology Netherlands Electrical and Computer Engineering University of Southern California United States Robotics Institute Carnegie Mellon University United States

Robots must operate safely when deployed in novel and human-centered environments, like homes. Current safe control approaches typically assume that the safety constraints are known a priori, and thus, the robot can precompute a corresponding safety controller. While this may make sense for some safety constraints (e.g., avoiding collision with walls by analyzing a floor plan), other constraints are more complex (e.g., spills), inherently personal, context-dependent, and can only be identified at deployment time when the robot is interacting in a specific environment and with a specific person (e.g., fragile objects, expensive rugs). Here, language provides a flexible mechanism to communicate these evolving safety constraints to the robot. In this work, we use vision language models (VLMs) to interpret language feedback and the robot’s image observations to continuously update the robot’s representation of safety constraints. With these inferred constraints, we update a Hamilton-Jacobi reachability safety controller online via efficient warm-starting techniques. Through simulation and hardware experiments, we demonstrate the robot’s ability to infer and respect language-based safety constraints with the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedback

Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction

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Towards Proactive Safe Human-Robot Collaborations via Data-E...

IEEE International Conference on robotics and Automation (ICRA)

作者： Ravi Pandya Zhuoyuan Wang Yorie Nakahira Changliu Liu Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot’s behavior, but such models do not account for the influence the robot has on the human’s actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot’s behavior based on a conditional model of the human that depends on the robot’s future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human’s intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and safe trajectory generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users’ interactions with a robot collaborator quantitatively and qualitatively.

关键词： Collaboration Predictive models Observers Behavioral sciences Trajectory Task analysis Robots

Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control

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arXiv 2024年

作者： Alvarez-Padilla, Juan Zhang, John Z. Kwok, Sofia Dolan, John M. Manchester, Zachary The Department of Electrical and Computer Engineering Carnegie Mellon University United States The Robotics Institute Carnegie Mellon University United States

This paper presents a system for enabling real-time synthesis of whole-body locomotion and manipulation policies for real-world legged robots. Motivated by recent advancements in robot simulation, we leverage the efficient parallelization capabilities of the MuJoCo simulator to achieve fast sampling over the robot state and action trajectories. Our results show surprisingly effective real-world locomotion and manipulation capabilities with a very simple control strategy. We demonstrate our approach on several hardware and simulation experiments: robust locomotion over flat and uneven terrains, climbing over a box whose height is comparable to the robot, and pushing a box to a goal position. To our knowledge, this is the first successful deployment of whole-body sampling-based MPC on real-world legged robot hardware. Experiment videos and code can be found at: ***. Copyright © 2024, The Authors. All rights reserved.

关键词： Biped locomotion

A study on an applied behavior analysis-based robot-mediated listening comprehension intervention for ASD

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Paladyn 2021年第1期12卷 31-46页

作者： Geoffrey Louie, Wing-Yue Korneder, Jessica Abbas, Ibrahim Pawluk, Cristyn Intelligent Robotics Laboratory Department of Electrical and Computer Engineering Oakland University RochesterMI48307 United States Applied Behavior Analysis Clinic Department of Human Development and Child Studies Oakland University RochesterMI48307 United States Intelligent Robotics Laboratory Department of Electrical and Computer Engineering Oakland University RochesterMI48307 United States

Autism spectrum disorder (ASD) is a lifelong developmental condition that affects an individual’s ability to communicate and relate to others. Despite such challenges, early intervention during childhood development has shown to have positive long-term benefits for individuals with ASD. Namely, early childhood development of communicative speech skills has shown to improve future literacy and academic achievement. However, the delivery of such interventions is often time-consuming. Socially assistive robots (SARs) are a potential strategic technology that could help support intervention delivery for children with ASD and increase the number of individuals that healthcare professionals can positively affect. For SARs to be effectively integrated in real-world treatment for individuals with ASD, they should follow current evidence-based practices used by therapists such as Applied Behavior Analysis (ABA). In this work, we present a study that investigates the efficacy of applying well-known ABA techniques to a robot-mediated listening comprehension intervention delivered to children with ASD at a university-based ABA clinic. The interventions were delivered in place of human therapists to teach study participants a new skill as a part of their overall treatment plan. All the children participating in the intervention improved in the skill being taught by the robot and enjoyed interacting with the robot, as evident by high occurrences of positive affect as well as engagement during the sessions. One of the three participants has also reached mastery of the skill via the robot-mediated interventions. © 2021 Wing-Yue Geoffrey Louie et al., published by De Gruyter.

关键词： Human robot interaction