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Guided Learning from Demonstration for Robust Transferability

Guided Learning from Demonstration for Robust Transferabilit...

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IEEE international conference on robotics and automation (ICRA)

作者： Fouad Sukkar Victor Hernandez Moreno Teresa Vidal-Calleja Jochen Deuse UTS Robotics Institute and the Centre for Advanced Manufacturing School for Mechanical and Mechatronic Engineering University of Technology Sydney Ultimo NSW Australia Australian Cobotics Centre (ITTC for Collaborative Robotics in Advanced Manufacturing)

Learning from demonstration (LfD) has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications. Recent progress in LfD methods have put more emphasis in learning robustness than in guiding the demonstration itself in order to improve robustness. The latter is particularly important to consider when the target system reproducing the motion is structurally different to the demonstration system, as some demonstrated motions may not be reproducible. In light of this, this paper introduces a new guided learning from demonstration paradigm where an interactive graphical user interface (GUI) guides the user during demonstration, preventing them from demonstrating non-reproducible motions. The key aspect of our approach is determining the space of reproducible motions based on a motion planning framework which finds regions in the task space where trajectories are guaranteed to be of bounded length. We evaluate our method on two different setups with a six-degree-of-freedom (DOF) UR5 as the target system. First our method is validated using a seven-DOF Sawyer as the demonstration system. Then an extensive user study is carried out where several participants are asked to demonstrate, with and without guidance, a mock weld task using a hand held tool tracked by a VICON system. With guidance users were able to always carry out the task successfully in comparison to only 44% of the time without guidance.

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Approach to Intelligent Support in Educational Program Design Using X-Matrices and a Domain-Specific Language

Approach to Intelligent Support in Educational Program Desig...

引用

international Russian automation conference (RusAutoCon)

作者： Artem Kuzmin Alexey Dukhanov Semen Kraev Faculty of Digital Transformation ITMO University Saint Petersburg Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This paper presents an automation system for the design and evaluation of educational programs using the X-matrix approach and natural language processing technologies. A processing cycle was developed to provide recommendations. The evaluation of machine learning classifiers identified Random Forest, XGBoost, Naive Bayes, Light GBM, and CatBoost as optimal models. Features derived from similarity metrics between text descriptions were assessed, with the most suitable feature set selected. The achieved metrics demonstrated satisfactory precision, recall, and F1-score values. The developed models were utilized to predict elements in cross-matrices for new educational programs, providing logical explanations for the course-competency connections. Methodologists can make informed decisions based on the model's arguments. This research contributes to the automation of educational program design and offers valuable insights for improving program coherence and technology integration.

关键词： Measurement Educational programs automation Coherence Predictive models Natural language processing Bayes methods Random forests Domain specific languages

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Comparative Analysis of Listwise Reranking with Large Language models in Limited-Resource Language Contexts

Comparative Analysis of Listwise Reranking with Large Langua...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yanxin Shen Lun Wang Chuanqi Shi Shaoshuai Du Yiyi Tao Yixian Shen Hang Zhang Simon Fraser University Canada Duke University North Carolina USA University of California San Diego California USA University of Amsterdam Amsterdam Netherlands Johns Hopkins University Maryland USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Large Language models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. This study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages. We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingual contexts. Results indicate that these LLMs significantly outperform traditional baseline methods such as BM25-DT in most evaluation metrics, particularly in nDCG@10 and MRR@100. These findings highlight the potential of LLMs in enhancing reranking tasks for low-resource languages and offer insights into cost-effective solutions.

关键词： Measurement Translation Costs Large language models Robots Context modeling

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Global and Reactive Motion Generation with Geometric Fabric Command Sequences

Global and Reactive Motion Generation with Geometric Fabric ...

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IEEE international conference on robotics and automation (ICRA)

作者： Weiming Zhi Iretiayo Akinola Karl Van Wyk Nathan D. Ratliff Fabio Ramos NVIDIA USA School of Computer Science the University of Sydney Australia Robotics Institute Carnegie Mellon University Pittsburgh USA

Motion generation seeks to produce safe and feasible robot motion from start to goal. Various tools at different levels of granularity have been developed. On one extreme, sampling-based motion planners focus on completeness - a solution, if it exists, would eventually be found. However, produced paths are often of low quality, and contain superfluous motion. On the other, reactive methods optimise the immediate cost to obtain the next controls, producing smooth and legible motion that can quickly adapt to perturbations, uncertainties, and changes in the environment. However, reactive methods are highly local, and often produce motion that become trapped in non-convex regions of the environment. This paper contributes, Geometric Fabric Command Sequences, a method that lies in the middle ground. It can produce globally optimal motion that is smooth and intuitive, while being also reactive. We model motion via a reactive Geometric Fabric policy that ingests a sequence of attractor states, or commands, and then apply global optimisation over the space of commands. We postulate that solutions for different problems and scenes are highly transferable when conditioned on environmental features. Therefore, an implicit generative model is trained on solutions from optimisation and environment features in a self-supervised manner. That is, faced with multiple motion generation problems, the learning and optimisation are contained within the same loop: the optimisation generates labels for learning, while the learning improves the optimisation for the next problem, which in turn provides higher quality labels. We empirically validate our method in both simulation and on a real-world 6-DOF JACO arm.

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Multi-Level Action Tree Rollout (MLAT-R): Efficient and Accurate Online Multiagent Policy Improvement

Multi-Level Action Tree Rollout (MLAT-R): Efficient and Accu...

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IEEE international conference on robotics and automation (ICRA)

作者： Andrea Henshall Sertac Karaman Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA Faculty of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Rollout algorithms are renowned for their abilities to correct for the suboptimalities of offline-trained base policies. In the multiagent setting, performing online rollout can require an exponentially large number of optimizations with respect to the number of agents. One-agent-at-a-time algorithms offer computationally efficient approaches to guaranteed policy improvement; however, this improvement is with respect to a state value estimate derived from a potentially poor base policy. Monte Carlo tree search (MCTS) provably converges to the true state value estimates; however, the exponentially large search space often makes its online use limited. Here, we present the Multi-Level Action Tree Rollout (MLAT-R) algorithm. MLAT-R provides 1) provable improvement over a base policy, 2) policy improvement with respect to the true state value, 3) applicability to any number of agents, and 4) an action space that grows linearly with the number of agents rather than exponentially. In this paper, we outline the algorithm, sketch a proof of its improvement over a base policy, and evaluate its performance on a challenging problem for which the base policy cannot reach a terminal state. Despite the challenging experimental setup, our algorithm reached a terminal state in 86% of all experiments, compared to 31% for state-of-the-art one-agent-at-a-time algorithms. In experiments involving MCTS, MLAT-R reached a terminal state in 99% of experiments compared to 92% for MCTS. MLAT-R achieved these results while considering an exponentially smaller action space than MCTS.

关键词： Monte Carlo methods Accuracy Computational efficiency robotics and automation Optimization

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Efficient and Hybrid Decoder for Local Map Construction in Bird'-Eye-View

Efficient and Hybrid Decoder for Local Map Construction in B...

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IEEE international conference on robotics and automation (ICRA)

作者： Kun Tian Yun Ye Zheng Zhu Peng Li Guan Huang PhiGent Robotics Beijing China

High-definition maps are crucial perception elements for autonomous robot navigation systems, which can provide accurate scene layout and environment information for downstream motion prediction and planning control tasks. Traditional methods based on manual annotation or SLAM algorithms require massive labor efforts and time costs, which hinders the deployment of practical applications. Online construction of local maps from on-board cameras offers an alternative solution. Aiming at the problems of unsatisfying precision and redundant computation of HDMapNet, we propose an efficient and hybrid decoder (EHD) that consists of a CNN-based segmentation (Seg) head and a query-based lane detection head (QLD). Specifically, the Seg head outputs pixel-level semantic maps, and QLD predicts instance mask for each lane object through learnable query embeddings. The designed decoding method eliminates the cumulative error caused by inaccurate semantic maps and does not require additional clustering algorithm for post-processing. Through combining with a variety of bird's-eye-view (BEV) encoders, the effectiveness and efficiency of our EHD is demonstrated by extensive experiments. For segmentation task, the mIoU scores of semantic map can be improved by 1.3%∼2.9%. Additionally, the accuracy of lane detection is also significantly increased (more than 10.2% mAP) under all evaluation criteria. Since our method discards redundant post-processing, the inference speed is up to 22.71 FPS, which is 32 times faster than HDMapNet.

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Locate before Segment: Topology-guided Retinal Layer Segmentation in Optical Coherence Tomography Images

Locate before Segment: Topology-guided Retinal Layer Segment...

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IEEE international conference on robotics and automation (ICRA)

作者： Ye Lu Yutian Shen Xiaohan Xing Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Electrical Engineering The City University of Hong Kong Hong Kong SAR China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute of The Chinese University of Hong Kong Shenzhen China

Optical Coherence Tomography (OCT) is a non-invasive imaging technique that is instrumental in retinal disease diagnosis and treatment. Segmentation of retinal layers in OCT is an essential step, but remains challenging for common pixel-wise segmentation methods usually fail to obtain the correct layer topology. To tackle this challenge, we propose a novel Locate-to-Segment (L2S) framework to provide a layer region location guidance for pixel-wise labeling learning so as to obtain better segmentation with the correct topology and smooth boundaries. Specifically, a Structured Boundary Regression Network (SBRNet) is devised to first predict the surface positions. For effective learning on normal-size images, we design two regression branches to regress the top surface and eight layer widths separately in SBRNet to locate each layer region with absolutely correct orderings. Then, we take the prediction of SBRNet as an additional input for a common pixel-wise segmentation network to provide the guidance of correct topology. In this L2S manner, our framework takes merits of regression-based methods and pixel-wise labeling-based methods to obtain accurate segmentation with the correct topology and smooth continuous boundaries. Experimental results on a public retinal OCT dataset demonstrate the effectiveness of our method, outperforming state-of-the-art segmentation methods with the highest average Dice score of 90.29% and the lowest average MAD score of 0.782.

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Digital Performance Prediction Model of Power Grid Dispatching Business Based on BP Neural Network 22

Digital Performance Prediction Model of Power Grid Dispatchi...

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22nd international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2024

作者： Yin, Wanting Liu, Yuhang Wang, Zhimin Zhai, Yanpeng Sun, Yixin Xu, Fangyuan State Grid Energy Research Institute Co. Ltd Beijing102209 China School of Automation Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798331528072

In the wave of enterprise digital transformation, power grid enterprises also need to consider the performance of investment during the digital transformation investment process. In the decision-making process of investment in digital transformation of power grid dispatching business, traditional cost-benefit analysis methods are faced with complex and changeable inputs. parameters, the calculation amount is large and the efficiency is low. This paper proposes a digital investment return prediction method for future status warning of power grid dispatching business based on BP neural network. Through feature selection and machine learning technology, it improves the efficiency and accuracy of future status warning of power grid dispatching digital investment performance analysis. This paper first uses the feature selection method to select features that are highly correlated with the prediction target, and then uses a machine learning model to learn the complex relationship between the input highly correlated features and the output performance to replace the traditional mathematical model and achieve efficient and rapid performance analysis. The experimental results show the performance of BP neural network in predicting future status warning of power grid dispatching investment results, and also verify the important role of feature selection in optimizing model performance. © 2024 IEEE.

关键词： Prediction models

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service robotics

A Comparison of Prompt Engineering Techniques for Task Plann...

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IEEE-RAS international conference on Humanoid Robots

作者： Jonas Bode Bastian Pätzold Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Recent advances in Large Language models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt_Engineering.

关键词： Knowledge engineering Service robots Large language models Instruments Humanoid robots Reliability engineering Time measurement Planning Prompt engineering Tuning

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SPRING LIFE PREDICTION MODEL BASED ON STRESS RELAXATION THEORY 14

SPRING LIFE PREDICTION MODEL BASED ON STRESS RELAXATION THEO...

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14th international conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Wu, Wenle Zhang, Chao Wang, Shaoping Chen, Rentong Zhou, Yulong Liu, Qingxu School of Automation Science and Electrical Engineering Beihang University Beijing China Ningbo Institute of Technology Beihang University Ningbo China Beijing Institue of Mechanical Equipment Beijing China

ISBN: (纸本)9781837242672

Stress relaxation is a significant failure mode observed in springs, and constructing a life prediction model based on stress relaxation is an effective approach. However, recent research has primarily focused on expensive experimental methods to quantify stress relaxation values. In this study, finite element simulation experiments on spring stress relaxation were conducted using a creep constitutive model and stress relaxation theory. The findings indicate that the results from these finite element simulations align well with theoretical expectations, suggesting that such simulations can partially substitute costly physical experiments. Furthermore, by leveraging data from finite element simulations, a stress relaxation model was integrated, and parameter fitting was performed to enhance the accuracy of the stress relaxation model and improve life prediction capabilities. © The Institution of Engineering & Technology 2024.

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