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Proceedings of the 35th International Conference on Neural Information Processing systems

作者： Ingmar Schubert Danny Driess Ozgur S. Oguz Marc Toussaint Learning and Intelligent Systems Group TU Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781713845393

Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is imprecise or wrong. In this sense, the respective strengths and weaknesses of RL and model-based planners are complementary. In the present work, we investigate how both approaches can be integrated into one framework that combines their strengths. We introduce Learning to Execute (L2E), which leverages information contained in approximate plans to learn universal policies that are conditioned on plans. In our robotic manipulation experiments, L2E exhibits increased performance when compared to pure RL, pure planning, or baseline methods combining learning and planning.

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Learning to execute: Efficiently learning universal plan-conditioned policies in robotics

arXiv

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arXiv 2021年

作者： Schubert, Ingmar Driess, Danny Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group Tu Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany

Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is imprecise or wrong. In this sense, the respective strengths and weaknesses of RL and modelbased planners are complementary. In the present work, we investigate how both approaches can be integrated into one framework that combines their strengths. We introduce Learning to Execute (L2E), which leverages information contained in approximate plans to learn universal policies that are conditioned on plans. In our robotic manipulation experiments, L2E exhibits increased performance when compared to pure RL, pure planning, or baseline methods combining learning and planning. © 2021, CC BY.

关键词： Robot programming

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Mecanum Wheel Robotic Platform for Educational Purposes: A Cost-Effective Approach

Mecanum Wheel Robotic Platform for Educational Purposes: A C...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Emanuel Viana Vítor H. Pinto José Lima Gil Gonçalves ISEP & SYSTEC - DIGI2 Polytechnic Institute of Porto Research Center for Systems and Technologies Digital and Intelligent Industry Lab Porto Portugal FEUP & SYSTEC - DIGI2 University of Porto Research Center for Systems and Technologies Digital and Intelligent Industry Lab Porto Portugal CeDRI & IPB Research Centre in Digitalization and Intelligent Robotics Instituto Politécnico de Bragança Bragança Portugal

ISBN: (纸本)9781665490498

This paper presents a cost-effective approach of a mecanum wheel robotic platform for educational propose on the development of an autonomous or remote controlled mobile robot with a four-wheel mecanum drive train. The main structure of the mobile robot was developed in Solidworks and it was built using additive manufacturing to validate in a real scenario. The main objective of developing this type of mobile platform was the ability to transport different types of cargo or robotic arm on industrial spaces or on rough terrain, since the implemented suspension mechanism allows the wheels contact to the floor. Another important objective is the maneuverability and the capacity to be guided in various environments, a great advantage in this type of mobile platform. An additional advantage of the developed mobile robot is the easy way to reconfigure the structure for new acquired parts.

关键词： Mechatronics Automation Service robots Wheels Three-dimensional printing Manipulators Mobile robots

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Real-time Dexterous Telemanipulation with an End-Effect-Oriented Learning-based Approach

arXiv

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arXiv 2024年

作者： Wang, Haoyang Bai, He Zhang, Xiaoli Jung, Yunsik Bowman, Michel Tao, Lingfeng The Oklahoma State University 563 Engineering North StillwaterOK74078 United States The Colorado School of Mines Intelligent Robotics and Systems Lab 1500 Illinois St GoldenCO80401 United States The University of Pennsylvania 3400 Civic Center Boulevard Building 421 PhiladelphiaPA19104 United States

Dexterous telemanipulation is crucial in advancing human-robot systems, especially in tasks requiring precise and safe manipulation. However, it faces significant challenges due to the physical differences between human and robotic hands, the dynamic interaction with objects, and the indirect control and perception of the remote environment. Current approaches predominantly focus on mapping the human hand onto robotic counterparts to replicate motions, which exhibits a critical oversight: it often neglects the physical interaction with objects and relegates the interaction burden to the human to adapt and make laborious adjustments in response to the indirect and counter-intuitive observation of the remote environment. This work develops an End-Effects-Oriented Learning-based Dexterous Telemanipulation (EFOLD) framework to address telemanipulation tasks. EFOLD models telemanipulation as a Markov Game, introducing multiple end-effect features to interpret the human operator's commands during interaction with objects. These features are used by a Deep Reinforcement Learning policy to control the robot and reproduce such end effects. EFOLD was evaluated with real human subjects and two end-effect extraction methods for controlling a virtual Shadow Robot Hand in telemanipulation tasks. EFOLD achieved real-time control capability with low command following latency (delay Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic arms

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TactCapsNet: Tactile capsule network for object hardness recognition

TactCapsNet: Tactile capsule network for object hardness rec...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Fang, Senlin Mi, Tingting Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Wu, Hancheng Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

Hardness is one of the most essential tactile clues for robots to recognize objects. However, methods for robots to recognize hardness are limited. In this paper, based on the Capsule Network (CapsNet), we propose a novel tactile capsule network (TactCapsNet) for object hardness recognition. Specifically, we collect a tactile dataset on the silicone samples with three different shapes, and the silicone samples of each shape have thirteen hardness levels ranging from 0A (Shore A scale) to 60A at 5A intervals. Furthermore, we construct the tactile image as the input of the CapsNet to make full use of the spatio-temporal information of the tactile hardness dataset. The experimental results prove that the proposed approach achieves higher accuracy and quadratic weighted kappa (QWK) than support vector machine (SVM), long short-term memory (LSTM), convolutional neural network (CNN), and CapsNet. © 2021 IEEE.

关键词： Hardness

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From Simulation to Reality: CNN Transfer Learning for Scene Classification 10

From Simulation to Reality: CNN Transfer Learning for Scene ...

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10th IEEE International Conference on intelligent systems, IS 2020

作者： Bird, Jordan J. Faria, Diego R. Ekart, Aniko Ayrosa, Pedro P. S. Aston University Aston Robotics Vision and Intelligent Systems Lab Birmingham United Kingdom Universidade Estadual de Londrina Londrina Brazil

ISBN: (纸本)9781728154565

In this work, we show that both fine-tune learning and cross-domain sim-to-real transfer learning from virtual to real-world environments improve the starting and final scene classification abilities of a computer vision model. A 6-class computer vision problem of scene classification is presented from both videogame environments and photographs of the real world, where both datasets have the same classes. 12 networks are trained from 2, 4, 8, , 4096 hidden interpretation neurons following a fine-tuned VGG16 Convolutional Neural Network for a dataset of virtual data gathered from the Unity game engine and for a photographic dataset gathered from an online image search engine. 12 Transfer Learning networks are then benchmarked using the trained networks on virtual data as a starting weight distribution for a neural network to classify the real-world dataset. Results show that all of the transfer networks have a higher starting accuracy pre-training, with the best showing an improvement of +48.34% image classification ability and an average increase of +38.33% for the starting abilities of all hyperparameter sets benchmarked. Of the 12 experiments, nine transfer experiments showed an improvement over non-transfer learning, two showed a slightly lower ability, and one did not change. The best accuracy overall was obtained by a transfer learning model with a layer of 64 interpretation neurons scoring 89.16% compared to the non-transfer counterpart of 88.27%. An average increase of +7.15% was observed over all experiments. The main finding is that not only can a higher final classification accuracy be achieved, but strong classification abilities prior to any training whatsoever are also encountered when transferring knowledge from simulation to real-world data, proving useful domain knowledge transfer between the datasets © 2020 IEEE.

关键词： Computer vision

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How2comm: communication-efficient and collaboration-pragmatic multi-agent perception 23

How2comm: communication-efficient and collaboration-pragmati...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Dingkang Yang Kun Yang Yuzheng Wang Jing Liu Zhi Xu Rongbin Yin Peng Zhai Lihua Zhang Academy for Engineering and Technology Fudan University and Cognition and Intelligent Technology Laboratory (CIT Lab) Academy for Engineering and Technology Fudan University FAW (Nanjing) Technology Development Company Ltd Academy for Engineering and Technology Fudan University and Cognition and Intelligent Technology Laboratory (CIT Lab) and Engineering Research Center of AI and Robotics Ministry of Education and AI and Unmanned Systems Engineering Research Center of Jilin Province

Multi-agent collaborative perception has recently received widespread attention as an emerging application in driving scenarios. Despite the advancements in previous efforts, challenges remain due to various dilemmas in the perception procedure, including communication redundancy, transmission delay, and collaboration heterogeneity. To tackle these issues, we propose How2comm, a collaborative perception framework that seeks a trade-off between perception performance and communication bandwidth. Our novelties lie in three aspects. First, we devise a mutual information-aware communication mechanism to maximally sustain the informative features shared by collaborators. The spatial-channel filtering is adopted to perform effective feature sparsification for efficient communication. Second, we present a flow-guided delay compensation strategy to predict future characteristics from collaborators and eliminate feature misalignment due to temporal asynchrony. Ultimately, a pragmatic collaboration transformer is introduced to integrate holistic spatial semantics and temporal context clues among agents. Our framework is thoroughly evaluated on several LiDAR-based collaborative detection datasets in real-world and simulated scenarios. Comprehensive experiments demonstrate the superiority of How2comm and the effectiveness of all its vital components. The code will be released at https://***/ydk122024/How2comm.

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Hierarchical Whole-body Control of the cable-Suspended Aerial Manipulator endowed with Winch-based Actuation

Hierarchical Whole-body Control of the cable-Suspended Aeria...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yuri S. Sarkisov Andre Coelho Maihara G. Santos Min Jun Kim Dzmitry Tsetserukou Christian Ott Konstantin Kondak Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Robotics and Mechatronics Group University of Twente Enschede The Netherlands Dextrous Robotics Inc. Memphis United States Instituto Tecnológico de Aeronáutica São José dos Campos Brazil Intelligent Robotic Systems Lab Korea Advanced Institute of Science and Technology (KAIST) Daejeon Korea Automation and Control Institute TU Wien Vienna Austria

During operation, aerial manipulation systems are affected by various disturbances. Among them is a gravitational torque caused by the weight of the robotic arm. Common propeller-based actuation is ineffective against such disturbances because of possible overheating and high power consumption. To overcome this issue, in this paper we propose a winch-based actuation for the crane-stationed cable-suspended aerial manipulator. Three winch-controlled suspension rigging cables produce a desired cable tension distribution to generate a wrench that reduces the effect of gravitational torque. In order to coordinate the robotic arm and the winch-based actuation, a model-based hierarchical whole-body controller is adapted. It resolves two tasks: keeping the robotic arm end-effector at the desired pose and shifting the system center of mass in the location with zero gravitational torque. The performance of the introduced actuation system as well as control strategy is validated through experimental studies.

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Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules

arXiv

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arXiv 2024年

作者： Rahman, Md Abdur Barek, Md Abdul Riad, A.B.M. Kamrul Islam Rahman, Md Mostafizur Rashid, Md Bajlur Ambedkar, Smita Miaa, Md Raihan Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States IDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to varioud machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95%. Additionally, we obtained high accuracy from Support Vector Machine (99.79%), Random Forest (99.73%), and Naive Bayes (95.93%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development. © 2024, CC BY.

关键词： Embeddings

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Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach

Early Prediction of Cryptocurrency Price Decline: A Deep Lea...

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International Conference on Computer and Information Technology (ICCIT)

作者： Mst Shapna Akter Hossain Shahriar Md Abdur Rahman Muhammad Rahman Alfredo Cuzzocrea Department of Intelligent Systems and Robotics University of West Florida USA Center for Cybersecurity University of West Florida USA Department of Mathematics Jahangirnagar University Savar Dhaka Bangladesh Department of Computer Science Clayton State University USA iDEA Lab University of Calabria Rende Italy

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market.

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