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Context-Based Meta Reinforcement learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab University of Bonn the Center for Robotics Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Peg-in-hole assembly in unknown environments is a challenging task due to onboard sensor errors, which result in uncertainty and variations in task parameters such as the hole position and orientation. Meta Reinforcement learning (Meta RL) has been proposed to mitigate this problem as it learns how to quickly adapt to new tasks with different parameters. However, previous approaches either depend on a sample-inefficient procedure or human demonstrations to perform the task in the real world. Our work modifies the data used by the Meta RL agent and uses simple features that can be easily measured in the real world even with an uncalibrated camera. We further adapt the Meta RL agent to use data from a force/torque sensor, instead of the camera, to perform the assembly, using a small amount of training data. Finally, we propose a fine-tuning method that consistently and safely adapts to out-of-distribution tasks with parameters that differ by a factor of 10 from the training tasks. Our results demonstrate that the proposed data modification significantly enhances the training and adaptation efficiency and enables the agent to achieve 100% success in tasks with different hole positions and orientations. Experiments on a real robot confirm that both camera- and force/torque sensor-equipped agents achieve 100% success in tasks with unknown hole positions, matching their simulation performance and validating the approach’s robustness and applicability. Compared to the previous work with sample-inefficient adaptation, our proposed methods are 10 times more sample-efficient in the real-world tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of machines in Manufacturing Industry

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Systematic Literature Review on Industry Revolution 4.0 to P...

Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Sowmya P Sathish Kumar Ravichandran Rakshitha Department of Robotics and Artificial Intelligence NMAM Institute of Technology-Affiliated to NITTE(Deemed to be University) Nitte Karnataka India Department of Computer Science School of Engineering & Technology Christ University Bangalore Karnataka India Department of Artificial Intelligence and Machine Learning NMAM Institute of Technology-Affiliated to NITTE(Deemed to be University) Nitte Karnataka India

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance.

关键词： Manufacturing industries Productivity Production systems Systematics Companies Life estimation Market research

Anterior TMS Speeds up Responses in Perceptual Decision-making Task 6

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Anterior TMS Speeds up Responses in Perceptual Decision-maki...

6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

作者： Maksimenko, Vladimir Gordleeva, Susanna Grigorev, Nikita Savosenkov, Andrey Kuc, Alexander Udoratina, Anna Grubov, Vadim Kolchina, Anna Kurkin, Semen Kazantsev, Victor Hramov, Alexander Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Mathematics and Mechanics Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia Neurotechnology Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neuroscience and Cognitive Technology Lab. Innopolis University Innopolis Russia Nizhny Novgorod Russia

ISBN: (数字)9781665461740

ISBN: (纸本)9781665461740

Our preliminary behavioral experiments suggest that the response time decreases when subjects respond to the repeatedly presented visual stimuli. A potential explanation is that the brain preactivates neural ensembles responsible for stimulus processing. If so, activating these areas before the experiment may reduce the response time immediately, opening ways for exciting practical applications. To test this opportunity, we apply transcranial magnetic stimulation (TMS) before the subjects start performing a perceptual decision-making task. Having compared the response time between the TMS and control groups, we observed a significant change confirming our hypothesis. © 2022 IEEE.

关键词： Decision making

Regression with comparisons: escaping the curse of dimensionality with ordinal information

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The Journal of Machine Learning Research

The Journal of machine learning Research 2020年第1期21卷 6480-6533页

作者： Yichong Xu Sivaraman Balakrishnan Aarti Singh Artur Dubrawski Machine Learning Department Department of Statistics and Data Science Machine Learning Department Auton Lab The Robotics Institute Carnegie Mellon University Pittsburgh PA

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain alternative feedback, possibly at a low cost. A broad goal is to understand the usefulness of, and to design algorithms to exploit, this alternative feedback. In this paper, we consider a semi-supervised regression setting, where we obtain additional ordinal (or comparison) information for the unlabeled samples. We consider ordinal feedback of varying qualities where we have either a perfect ordering of the samples, a noisy ordering of the samples or noisy pairwise comparisons between the samples. We provide a precise quantification of the usefulness of these types of ordinal feedback in both nonparametric and linear regression, showing that in many cases it is possible to accurately estimate an underlying function with a very small labeled set, effectively escaping the curse of dimensionality. We also present lower bounds, that establish fundamental limits for the task and show that our algorithms are optimal in a variety of settings. Finally, we present extensive experiments on new datasets that demonstrate the efficacy and practicality of our algorithms and investigate their robustness to various sources of noise and model misspecification.

关键词： pairwise comparison ranking regression interactive learning

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

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arXiv 2021年

作者： Hartmann, Valentin N. Orthey, Andreas Driess, Danny Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Group TU Berlin Germany Department of Computer Engineering Bilkent University Turkey

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multirobot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world. Copyright © 2021, The Authors. All rights reserved.

关键词： robotics

Exploration via Planning for Information about the Optimal Trajectory

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arXiv 2022年

作者： Mehta, Viraj Char, Ian Abbate, Joseph Conlin, Rory Boyer, Mark D. Ermon, Stefano Schneider, Jeff Neiswanger, Willie Robotics Institute United States Machine Learning Department Carnegie Mellon University United States Princeton Plasma Physics Laboratory United States Princeton University United States Computer Science Department Stanford University United States

Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Changing functional connectivity during solving cognitive tasks: fNIRS study

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Changing functional connectivity during solving cognitive ta...

Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics

作者： Badarin, A.A. Antipov, V.M. Grubov, V.V. Kurkin, S.A. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia Department of Theoretical Cybernetics Saint Petersburg State University University Embankment 7/9 Saint Petersburg199034 Russia

ISBN: (数字)9781510653702

ISBN: (纸本)9781510653696

In this paper, we present an analysis of the dynamics of functional connectivity of the cerebral cortical network using near-infrared spectroscopy during human solutions to simple cognitive tasks. A task-based on the Sternberg paradigm was chosen to provide a cognitive load. We identified statistically significant changes in the communication forces obtained on the basis of the analysis of oxyhemoglobin signals during the experiment. We found that in the course of the experiment, there is a restructuring of the functional network, which is accompanied by a decrease in the average connectivity strength between the cortical areas under study. We showed that there is a correlation between the subjective evaluation of fatigue degree and the characteristics of the identified functional neural network. © 2022 SPIE.

关键词： Near infrared spectroscopy

WCEBleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation

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arXiv 2024年

作者： Handa, Palak Dhir, Manas Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Gunjan, Deepak Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Gastroenterology HNU All India Institute of Medical Sciences Delhi India

Objective: Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of automatic classification, detection, and segmentation of bleeding and non-bleeding frames is currently lacking. Methods: The present work focused on development of a medically annotated WCE dataset called WCEbleedGen for automatic classification, detection, and segmentation of bleeding and non-bleeding frames. It comprises 2,618 WCE bleeding and non-bleeding frames which were collected from various internet resources and existing WCE datasets. A comprehensive benchmarking and evaluation of the developed dataset was done using nine classification-based, three detection-based, and three segmentation-based deep learning models. Results: The dataset is of high-quality, is class-balanced and contains single and multiple bleeding sites. Overall, our standard benchmark results show that Visual Geometric Group (VGG) 19, You Only Look Once version 8 nano (YOLOv8n), and Link network (Linknet) performed best in automatic classification, detection, and segmentation-based evaluations, respectively. Conclusion and significance: Automatic bleeding diagnosis is crucial for WCE video interpretations. This diverse dataset will aid in developing of real-time, multi-task learning-based innovative solutions for automatic bleeding diagnosis in WCE. The dataset and code are publicly available here. © 2024, CC BY-NC-SA.

关键词： Endoscopy

SENSORIMOTOR GRAPH: Action-conditioned graph neural network for learning robotic soft hand dynamics

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arXiv 2021年

作者： Almeida, João Damião Mano Schydlo, Paul Dehban, Atabak Santos-Victor, José Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Machine Learning Department Carnegie Mellon University United States

Soft robotics is a thriving branch of robotics which takes inspiration from nature and uses affordable flexible materials to design adaptable non-rigid robots. However, their flexible behavior makes these robots hard to model, which is essential for a precise actuation and for optimal control. For system modelling, learning-based approaches have demonstrated good results, yet they fail to consider the physical structure underlying the system as an inductive prior. In this work, we take inspiration from sensorimotor learning, and apply a Graph Neural Network to the problem of modelling a non-rigid kinematic chain (i.e. a robotic soft hand) taking advantage of two key properties: 1) the system is compositional, that is, it is composed of simple interacting parts connected by edges, 2) it is order invariant, i.e. only the structure of the system is relevant for predicting future trajectories. We denote our model as the "Sensorimotor Graph" since it learns the system connectivity from observation and uses it for dynamics prediction. We validate our model in different scenarios and show that it outperforms the non-structured baselines in dynamics prediction while being more robust to configurational variations, tracking errors or node failures. © 2021, CC BY.

关键词： Kinematics