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Active Visuo-Haptic Object Shape Completion

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arXiv 2022年

作者： Rustler, Lukas Lundell, Jens Behrens, Jan Kristof Kyrki, Ville Hoffmann, Matej The Department of Cybernetics Faculty of Electrical Engineering CTU Prague Czech Republic The Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland The Czech Institute of Informatics Robotics and Cybernetics CTU Prague Czech Republic

Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this work opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

AI Enabled Automatic Mobile Robot intelligent Navigation in Construction with Obstacle Awareness

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IEEE Transactions on automation Science and Engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

Constrained Generative Sampling of 6-DoF Grasps

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arXiv 2023年

作者： Lundell, Jens Verdoja, Francesco Le, Tran Nguyen Mousavian, Arsalan Fox, Dieter Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland KTH Royal Institute of Technology Sweden NVIDIA Corporation United States Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object’s body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10–15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website. Copyright © 2023, The Authors. All rights reserved.

关键词： Bottles

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

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

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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Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots

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arXiv 2022年

作者： Grando, Ricardo B. de Jesus, Junior C. Kich, Victor A. Kolling, Alisson H. Guerra, Rodrigo S. Drews, Paulo L.J. The Technological University of Uruguay Uruguay NAUTEC - Intelligent Robotics and Automation Group Center for Computational Science Federal University of Rio Grande - FURG RS Brazil The Universidade Federal de Santa Maria - UFSM RS Brazil

Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide variety of robots. Previous works showed that these Deep-RL algorithms can be applied to perform mapless navigation of mobile robots in general. However, they tend to use simple sensing strategies since it has been shown that they perform poorly with a high dimensional state spaces, such as the ones yielded from image-based sensing. This paper presents a comparative analysis of two Deep-RL techniques - Deep Deterministic Policy Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing tasks of mapless navigation for mobile robots. We aim to contribute by showing how the neural network architecture influences the learning itself, presenting quantitative results based on the time and distance of navigation of aerial mobile robots for each approach. Overall, our analysis of six distinct architectures highlights that the stochastic approach (SAC) better suits with deeper architectures, while the opposite happens with the deterministic approach (DDPG). © 2022, CC BY.

关键词： Mobile robots

Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements

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arXiv 2024年

作者： Kruzliak, Andrej Hartvich, Jiri Patni, Shubhan P. Rustler, Lukas Behrens, Jan Kristof Abu-Dakka, Fares J. Mikolajczyk, Krystian Kyrki, Ville Hoffmann, Matej Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Mechanical Engineering Department Faculty of Engineering New York University Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland

This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, containing over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations. © 2024, CC BY.

关键词： Grippers

A biofuel risk management model for biomass-based heat sources considering supply-demand-side uncertainties

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A biofuel risk management model for biomass-based heat sourc...

12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)

作者： D. Fu T. Zhou T. Yang T. Zhang J. Dong Key Laboratory of Networked Control Systems Institutes for Robotics and Intelligent Manufacturing Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang People's Republic of China Technical Management Division Guodian Technology & Environment Group Co Ltd. Beijing People's Republic of China Digital Factory Department Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang People's Republic of China

Biofuel-based heat sources are gradually penetrated into heating systems in Northern China in the background of carbon neutrality. It is essential to conduct biofuel risk management study, not only for enhancing the ability of continuous heat supply for heating systems, but also improving the ability of effective cost control. A biofuel risk management model is thus proposed innovatively for biomass-based heat sources considering supply-demand-side uncertainties. The developed model integrates Chance-constrained programming, Two-stage stochastic programming and the traditional linear programming into a general framework. After that, the developed model is applied in to a real heat-source case of biofuel risk management under uncertainties and the corresponding solutions on biofuel risk management are generated. The beneficial results of biofuel allocation pattern can be obtained for decision-makers to provide the fuel management support under different risk levels. Besides, a trade-off analysis between the constraint-violation risk and the system cost can also be provided to conduct in-depth analysis.

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DROPO: Sim-to-Real Transfer with Offline Domain Randomization

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arXiv 2022年

作者： Tiboni, Gabriele Arndt, Karol Kyrki, Ville Dept. of Control and Computer Engineering Politecnico di Torino Francesco Ferrucci Street 112 Torino10141 Italy Intelligent Robotics Group Dept. of Electrical Engineering and Automation Aalto University Maarintie 8 Espoo02150 Finland

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation;however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data using a likelihood-based approach. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodeled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Unsupervised Learning Method for Encoder-Decoder-Based Image Restoration 9th

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Unsupervised Learning Method for Encoder-Decoder-Based Image...

9th Brazilian Conference on intelligent Systems, BRACIS 2020

作者： Mello, Claudio D. Messias, Lucas R. V. Drews-Jr, Paulo Lilles Jorge Botelho, Silvia S. C. NAUTEC - Intelligent Robotics and Automation Group Center for Computational Science - C3 Federal University of Rio Grande - FURG Rio GrandeRS96.203-000 Brazil

ISBN: (纸本)9783030613761

The restoration of a corrupted image is a challenge to computer vision and image processing. In hazy, underwater and medical images, the lack of paired images lead the state of the art to synthesize datasets. The Generative Adversarial Networks (GANs) are widely used in these cases. However, computational cost and training instability are current concerns. We present an unsupervised learning algorithm that does not requires paired dataset to train encoder-decoder-like neural network for image restoration. An encoder-decoder learn to represent its input data in a latent representation and reconstruct then in the output. During the training stage, our algorithm applies the encoder-decoder output image to a degradation block that reinforces its degradation. The degraded and input images are matched using a loss function. After the training process, we obtain a restored image from the decoder. We used ill-exposed images to evaluate and validate our algorithm. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction