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IEEE Southeastcon

作者： Joshua M. Zutell David C. Conner Philipp Schillinger Capable Humanitarian Robotics and Intelligent Systems Lab (CHRISLab) Christopher Newport University Newport News VA Bosch Center for Artificial Intelligence Renningen Germany

Hierarchical Finite State Machines (HFSM) are commonly used as high-level behavioral control strategies for robotic systems. Over the years a number of behavior engines have been developed for the original Robot Operating System (ROS 1), including the popular Flexible Behavior Engine (FlexBE). In recent years a new ROS 2 system has been developed to improve communication performance by using the Data Distribution Service (DDS) protocol. This paper describes the conversion of both FlexBE and the open-source Flexible Navigation system to the latest ROS 2 release, and our experiments with the new system. Both ROS 2 conversions are released as open-source and available for use by the robotics community.

关键词： Protocols Navigation Operating systems Collaboration Automata User interfaces systems support

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Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models

arXiv

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arXiv 2022年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim The Machine Learning and Robotics Lab University of Stuttgart Germany The Humans to Robots Motions Research Group University of Stuttgart Germany The Learning and Intelligent Systems Lab TU Berlin Germany

Numerical optimization has become a popular approach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efficiency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modifiers to a differentiable human predictive model based on a dedicated recurrent neural network. These modifiers allow to change the human prediction within motion optimization. We empirically evaluate our method using the publicly available MoGaze [1] dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other. © 2022, CC BY.

关键词： Forecasting

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Towards Analysis and Interpretation of Large Language Models for Arithmetic Reasoning

Towards Analysis and Interpretation of Large Language Models...

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Swiss Conference on Data Science (SDS)

作者： Mst. Shapna Akter Hossain Shahriar Alfredo Cuzzocrea Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Center for Cybersecurity University of West Florida Pensacola FL USA iDEA Lab University of Calabria Rende Italy Department of Computer Science University of Paris City Paris France

ISBN: (数字)9798350309294

ISBN: (纸本)9798350309300

Large Language Models (LLMs) have recently conquered the research scene, with particular regards to the Transformer architecture in the context of arithmetic reasoning. In this so-delineated scenario, this paper puts the basis for a causal mediation analysis about the approach of Transformer-based LLMs to complex arithmetic problems. In particular, we try to discover which parameters are crucial for complex reasoning tasks such as model activations. Our preliminary results state that, for complex arithmetic operations, information is channeled from mid-layer activations to the final token through enhanced attention mechanisms. Preliminary experiments are reported.

关键词： Attention mechanisms Large language models Data science Transformers Cognition Mediation Arithmetic

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Self-Supervised Learning of Scene-Graph Representations for Robotic Sequential Manipulation Planning 4

Self-Supervised Learning of Scene-Graph Representations for ...

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4th Conference on Robot Learning, CoRL 2020

作者： Nguyen, Son-Tung Oguz, Ozgur S. Hartmann, Valentin N. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Group TU Berlin Germany

We present a self-supervised representation learning approach for visual reasoning and integrate it into a nonlinear program formulation for motion optimization to tackle sequential manipulation tasks. Such problems have usually been addressed by combined task and motion planning approaches, for which spatial relations and logical rules that rely on symbolic representations have to be predefined by the user. We propose to learn relational structures by leveraging visual perception to alleviate the resulting knowledge acquisition bottleneck. In particular, we learn constructing scene-graphs, that represent objects ("red box"), and their spatial relationships ("yellow cylinder on red box"). This representation allows us to plan high-level discrete decisions effectively using graph search algorithms. We integrate the visual reasoning module with a nonlinear optimization method for robot motion planning and verify its feasibility on the classic blocks-world domain. Our proposed framework successfully finds the sequence of actions and enables the robot to execute feasible motion plans to realize the given tasks. © 2020 Proceedings of Machine Learning Research. All rights reserved.

关键词： Motion planning

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Learning Fast and Precise Pixel-to-Torque Control

arXiv

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arXiv 2022年

作者： Bleher, Steffen Heim, Steve Trimpe, Sebastian Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart Germany Biomimetic Robotics Lab Massachusetts Institute of Technology Cambridge United States Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen Germany

In the field, robots often need to operate in unknown and unstructured environments, where accurate sensing and state estimation (SE) becomes a major challenge. Cameras have been used to great success in mapping and planning in such environments, as well as complex but quasi-static tasks such as grasping, but are rarely integrated into the control loop for unstable systems. Learning pixel-to-torque control promises to allow robots to flexibly handle a wider variety of tasks. Although they do not present additional theoretical obstacles, learning pixel-to-torque control for unstable systems that that require precise and high bandwidth control still poses a significant practical challenge, and best practices have not yet been established. To help drive reproducible research on the practical aspects of learning pixel-to-torque control, we propose a platform that can flexibly represent the entire process, from lab to deployment, for learning pixel-to-torque control on a robot with fast, unstable dynamics: the vision-based Furuta pendulum. The platform can be reproduced with either off-the-shelf or custom-built hardware. We expect that this platform will allow researchers to quickly and systematically test different approaches, as well as reproduce and benchmark case studies from other labs. We also present a first case study on this system using DNNs which, to the best of our knowledge, is the first demonstration of learning pixel-to-torque control on an unstable system with update rates faster than 100 Hz. A video synopsis can be found online at https://***/S2llScfG-8E, and in the supplementary material. © 2022, CC BY.

关键词： Torque control

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Md Abdur Rahman Hossain Shahriar Fan Wu Alfredo Cuzzocrea Dept. of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Center for Cybersecurity University of West Florida Pensacola FL USA Department of Computer Science Tuskegee University Tuskegee AL USA iDEA Lab University of Calabria Rende Italy

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities.

关键词： Support vector machines Logistic regression Accuracy Large language models Bidirectional control Transformers Encoding Multilingual Tuning Random forests

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Co-Optimizing Robot, Environment, and Tool Design via Joint Manipulation Planning

Co-Optimizing Robot, Environment, and Tool Design via Joint ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Toussaint Jung-Su Ha Ozgur S. Oguz Learning & Intelligent Systems Lab TU Berlin Germany Max Planck Institute for Intelligent Systems Germany Machine Learning & Robotics Lab University of Stuttgart Germany

Existing work on sequential manipulation planning and trajectory optimization typically assumes the robot, environment and tools to be given. However, in particular in industrial applications, it is highly interesting to ask, what would be an optimal robot design, tool shape, or robot station geometry for a particular ensemble of manipulation tasks. To tackle this problem we propose a formulation to jointly optimize over static design parameters and the sequential manipulation trajectory. We can include optimization objectives such as penalizing velocities (path length) and joint torques. Our evaluations show that design optimization can significantly improve on such metrics. For instance, in a wrench tool demonstration scenario we show that the shape of the wrench tool as well as design of the robot can be optimized to allow for exerting a necessary external torque with minimal effort.

关键词： Measurement Torque Service robots Shape Tools Programming Planning

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Rehabilitation Training of Spinal Cord Injured Individuals by a Self-Balancing Exoskeleton Robot

Rehabilitation Training of Spinal Cord Injured Individuals b...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Wentao Li Jinke Li Yong He Dingkui Tian Feng Li Jianquan Sun Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China China Construction Third Bureau First Engineering MEP Co. Ltd. Shenzhen China

Physical therapy plays an important role in the rehabilitation for spinal cord injured individuals, and exoskeleton can assist with this process. In this paper, a self-balancing exoskeleton robot is introduced, which can provide assist not only in gait training, but also in balance training. In addition, it allows individuals walk with it without other assistive devices. This exoskeleton is designed to have a high degree of compatibility with humans. The structure and control system of the exoskeleton is introduced and the forward and inverse kinematic are solved, which enables the robot to perform various rehabilitation training actions as required. Experiments with a male subject have shown that this exoskeleton achieves the predicted results in gait training and balance training.

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Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation

Learning Efficient Constraint Graph Sampling for Robotic Seq...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joaquim Ortiz-Haro Valentin N. Hartmann Ozgur S. Oguz Marc Toussaint Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Germany Max Planck Institute for Intelligent Systems Germany

Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality constraints, each of which depends only on some variables. Such problems are at the core of efficient and robust sequential robot manipulation planning. Naive sequential conditional sampling of individual variables, as well as fully joint sampling of all variables at once (e.g., leveraging optimization methods), can be highly inefficient and non-robust. We propose a novel framework to learn how to break the overall problem into smaller sequential sampling problems. Specifically, we leverage Monte-Carlo Tree Search to learn assignment orders for the variable-subsets, in order to minimize the computation time to generate feasible full samples. This strategy allows us to efficiently compute a set of diverse valid robot configurations for mode-switches within sequential manipulation tasks, which are waypoints for subsequent trajectory optimization or sampling-based motion planning algorithms. We show that the learning method quickly converges to the best sampling strategy for a given problem, and outperforms user-defined orderings or fully joint optimization, while providing a higher sample diversity. Video: https://***/mCNdvjTbHNI

关键词： Manifolds Learning systems Costs Monte Carlo methods Conferences Optimization methods Planning

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Teaching a Robot Where to Park: A Scalable Crowdsourcing Approach

Teaching a Robot Where to Park: A Scalable Crowdsourcing App...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： De’Aira Bryant Tiago Etiene Ayanna Howard William D. Smart Dylan F. Glas College of Computing at Georgia Institute of Technology Experimental work performed during an Amazon Lab126 internship Amazon Lab126 College of Engineering The Ohio State University Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University

For social robots to successfully integrate into daily life in home environments, they will need reliable models of the way people perceive and use space in the home. This paper explores the problem of obtaining annotated training data at scale for subjective judgments about spatial locations. Focusing on the use case of identifying good and bad parking spots for a social robot operating in a home environment, two experiments are presented. The first study shows that the presentation of context-rich 3D images to human annotators yields notably different outcomes from those obtained when using 2D robot navigation maps. We attribute the source of these differences to a set of features visible only in the 3D views and introduce a technique for labeling these features on the 2D maps. The second study reveals that using labeled 2D maps produces annotation data very similar to that obtained using 3D images. Since a labeled 2D map can be generated at a fraction of the cost of a full set of 3D views, we recommend this method as a scalable approach to collecting subjective spatial data annotations in everyday environments.

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