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9th international conference on robotics and automation Engineering, ICRAE 2024

作者： Muresan, Vlad Stancioi, Cristina-Maria Abrudean, Mihail Miron, Radu Technical University of Cluj-Napoca Automation Department Cluj-Napoca Romania

ISBN: (纸本)9798331518301

This paper presents a comprehensive solution for controlling and modeling power delivery in a microgrid powered by photovoltaic panels, with a focus on robust operation both in normal conditions and under faults. The system uses advanced mathematical models to describe key system components, which serve as reference points for a fault detection mechanism. An adaptive control strategy regulates power output and rejects disturbances, ensuring fault tolerance. The DC/DC converter model is based on a transfer function structure with variable coefficients, and a feedforward neural network is employed to dynamically adjust these coefficients based on the duty cycle. The control framework also incorporates a compensator to prevent instability when converter parameters deviate from nominal values, enhancing the system's fault resilience. The integrated control algorithms for the boost DC/DC converter and single-phase inverter are designed for real-time implementation on 32-bit ARM processors, providing an efficient, fault-tolerant solution for microgrid management. © 2024 IEEE.

关键词： ARM processors

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Imitation learning using Gaussian mixture models and Dynamic Movement Primitives for rehabilitation exoskeletons: a comparison 10

Imitation learning using Gaussian mixture models and Dynamic...

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10th IEEE RAS/EMBS international conference for Biomedical robotics and Biomechatronics (BioRob)

作者： Luciani, Beatrice Costante, Simone Braghin, Francesco Pedrocchi, Alessandra Gandolla, Marta Politecn Milan WE COBOT Lab Polo Terr Lecco Via G Previati 1-C I-23900 Lecce Italy Politecn Milan Dept Mech Engn Via La Masa 1 I-20156 Milan Italy

ISBN: (纸本)9798350386523;9798350386530

Trajectory generation for upper-limb rehabilitation exoskeletons is crucial for post-stroke therapy success. Learning by Demonstrations (LbD) techniques have emerged as powerful tools to extract smooth and accurate human skills from physiotherapists' demonstrated movements, increasing the benefits of robotic therapy and therapists' acceptability. Nevertheless, so far no concordance on the best approach to perform LbD for rehabilitation exercises has been reached. In this work, we perform a detailed analysis to compare the performances and advantages of two well-known LbD methods: the deterministic approach called Dynamic motion primitives (DMPs) and the probabilistic Gaussian mixture models and regression method (GMM-GMR). We validated and compared the proposed approaches using multiple databases of trajectories performed by physiotherapists, generated using the AGREE exoskeleton by Politecnico di Milano. Results show that the implemented methods outperform the corresponding state-of-the-art polynomial trajectories in precision and human likeness, evaluated through metrics such as Spectral Arc Length and Logarithmic Dimensionless Jerk. Our analysis reveals that both methods have potential, but for different purposes. GMM-GMR excels in precisely reproducing therapists' movements and should be preferred when the clinician wants to enhance particular gestures. DMPs give smoother trajectories, especially when dealing with smaller datasets, and can better comply with therapy timings.

关键词： Exoskeleton (robotics)

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Astrobee ISS Free-Flyer Datasets for Space Intra-Vehicular Robot Navigation Research

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IEEE robotics AND automation LETTERS 2024年第4期9卷 3307-3314页

作者： Kang, Suyoung Soussan, Ryan Lee, Daekyeong Coltin, Brian Vargas, Andres Mora Moreira, Marina Hamilton, Kathryn Garcia, Ruben Bualat, Maria Smith, Trey Barlow, Jonathan Benavides, Jose Jeong, Eunju Kim, Pyojin Sookmyung Womens Univ Dept Elect Engn Seoul 04310 South Korea NASA Ames Res Ctr Moffett Field CA 94035 USA KBR Inc Houston TX 77058 USA Sookmyung Womens Univ Dept Mech Syst Engn Seoul 04310 South Korea Gwangju Inst Sci & Technol GIST Sch Mech Engn Gwangju 61005 South Korea

We present the first annotated benchmark datasets for evaluating free-flyer visual-inertial localization and mapping algorithms in a zero-g spacecraft interior. The Astrobee free-flying robots that operate inside the international Space Station (ISS) collected the datasets. Space intra-vehicular free-flyers face unique localization challenges: their IMU does not provide a gravity vector, their attitude is fully arbitrary, and they operate in a dynamic, cluttered environment. We extensively evaluate state-of-the-art visual navigation algorithms on these challenging Astrobee datasets, showing superior performance of classical geometry-based methods over recent data-driven approaches. The datasets include monocular images and IMU measurements, with multiple sequences performing a variety of maneuvers and covering four ISS modules. The sensor data is spatio-temporally aligned, and extrinsic/intrinsic calibrations, ground-truth 6-DoF camera poses, and detailed 3D CAD models are included to support evaluation. The datasets are available at: https://***/.

关键词： Navigation Robots Cameras Visualization Robot vision systems Space vehicles Location awareness Data Sets for SLAM Space robotics and automation SLAM Autonomous Vehicle Navigation

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Autonomous Discovery of Robot Structure and Motion Control Through Large Vision models 11

Autonomous Discovery of Robot Structure and Motion Control T...

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11th IEEE international conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE international conference on robotics, automation and Mechatronics (RAM)

作者： Li, Xiaohui Liu, Lian Zhang, Zhao Guo, Xiaoyu Cui, Jinqiang Peng Cheng Lab Shenzhen 518055 Peoples R China South China Univ Technol Guangzhou 511442 Peoples R China

ISBN: (纸本)9798350364200;9798350364194

This paper introduces an algorithm for autonomous self-modeling of robots through the integration of Large Vision Model (LVM) and Large Language Model (LLM). Our approach differs from traditional robotic approaches in that it enables robots to independently discover and refine their own body structure and control strategies using only partial information. Through a symbiotic process that includes LLM's ability to generate predictive control code based on finite prompts, and LVM's visual reasoning to validate and improve those predictions, our algorithm facilitates a self-learning loop. This cycle is characterized by an inner loop of assumptions, observations, and adjustments, supplemented by an outer loop that gradually increases the information provided until convergence is reached. The effectiveness of the process was quantified by measuring the difference between the expected and actual joint motion as a cost function to determine the minimum feasible prompt (MVP). Simulation results indicate that the algorithm is capable of self-modeling of with minimal initial information.

关键词： robotics Large Language Model Visual Reasoning Embodied AI

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Australasian conference on robotics and automation, ACRA 2024

Australasian Conference on Robotics and Automation, ACRA 202...

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2024 Australasian conference on robotics and automation, ACRA 2024

The proceedings contain 36 papers. The topics discussed include: attention-enhanced lightweight hourglass network for human pose estimation;workspace and cutting force requirements for an automated grapevine pruner;grasping by parallel shape matching;robot drawing on a moving paperboard;quadtree-based spatial-visual memory for object navigation;development of a 3D, high-fidelity simulator for autonomous racing of F1Tenth cars;real-time learning-based gesture generation for pepper robot using audio;benchmarking reinforcement learning methods for dexterous robotic manipulation with a three-fingered gripper;ground robotic sampling system for intelligent pasture mapping;and facilitating AI alignment: weak-to-strong generalization with debate mechanisms in language models.

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Online Tool Selection with Learned Grasp Prediction models

Online Tool Selection with Learned Grasp Prediction Models

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IEEE international conference on robotics and automation (ICRA)

作者： Rohanimanesh, Khashayar Metzger, Jake Richards, William Tamar, Aviv Osaro Inc San Francisco CA 94105 USA Accenture Inc San Francisco CA 94105 USA Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9798350323658

Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. Tool-change, however, takes time. Choosing the order of grasps to perform, and corresponding tool-change actions, can improve system throughput;this is the topic of our work. The main challenge in planning tool change is uncertainty - we typically cannot see objects in the bin that are currently occluded. Inspired by queuing and admission control problems, we model the problem as a Markov Decision Process (MDP), where the goal is to maximize expected throughput, and we pursue an approximate solution based on model predictive control, where at each time step we plan based only on the currently visible objects. Special to our method is the idea of void zones, which are geometrical boundaries in which an unknown object will be present, and therefore cannot be accounted for during planning. Our planning problem can be solved using integer linear programming (ILP). However, we find that an approximate solution based on sparse tree search yields near optimal performance at a fraction of the time. Another question that we explore is how to measure the performance of tool-change planning: we find that throughput alone can fail to capture delicate and smooth behavior, and propose a principled alternative. Finally, we demonstrate our algorithms on both synthetic and real world bin picking tasks.

关键词： Markov processes

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TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics models 39

TartanDrive: A Large-Scale Dataset for Learning Off-Road Dyn...

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IEEE international conference on robotics and automation (ICRA)

作者： Triest, Samuel Sivaprakasam, Matthew Wang, Sean J. Wang, Wenshan Johnson, Aaron M. Scherer, Sebastian Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Pittsburgh Elect & Comp Engn Dept Pittsburgh PA USA Carnegie Mellon Univ Mech Engn Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781728196817

We present TartanDrive, a large scale dataset for learning dynamics models for off-road driving. We collected a dataset of roughly 200,000 off-road driving interactions on a modified Yamaha Viking ATV with seven unique sensing modalities in diverse terrains. To the authors' knowledge, this is the largest real-world multi-modal off-road driving dataset, both in terms of number of interactions and sensing modalities. We also benchmark several state-of-the-art methods for model-based reinforcement learning from high-dimensional observations on this dataset. We find that extending these models to multi-modality leads to significant performance on off-road dynamics prediction, especially in more challenging terrains. We also identify some shortcomings with current neural network architectures for the off-road driving task. Our dataset is available at https://***/castacks/tartan drive.

关键词： automation Neural networks Reinforcement learning Predictive models Benchmark testing Robot sensing systems Sensors

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Barrier Forming: Separating Polygonal Sets with Minimum Number of Lines 39

Barrier Forming: Separating Polygonal Sets with Minimum Numb...

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IEEE international conference on robotics and automation (ICRA)

作者： Peng, Si Wei Yu, Jingjin Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9781728196817

In this work, we carry out structural and algorithmic studies of a problem of barrier forming: selecting the minimum number of straight line segments (barriers) that separate several sets of mutually disjoint objects in the plane. The problem models the optimal placement of line sensors (e.g., infrared laser beams) for isolating many types of regions in a pair-wise manner for practical purposes (e.g., guarding against intrusions). The problem is NP-hard even if we want to find the minimum number of lines to separate two sets of points in the plane. Under the umbrella problem of barrier forming with minimum number of line segments, three settings are examined: barrier forming for point sets, point sets with polygonal obstacles, polygonal sets with polygonal obstacles. We describe methods for computing the optimal solution for the first two settings with the assistance of mathematical programming, and provide a 2-OPT solution for the third. We demonstrate the effectiveness of our methods through extensive simulations.

关键词： NP-hard problem Particle separators Heuristic algorithms Filtering algorithms Laser modes Mathematical models Sensors

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Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human models 39

Safety Assurances for Human-Robot Interaction via Confidence...

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IEEE international conference on robotics and automation (ICRA)

作者： Tian, Ran Sun, Liting Bajcsy, Andrea Tomizuka, Masayoshi Dragan, Anca D. Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781728196817

An outstanding challenge with safety methods for human-robot interaction is reducing their conservatism while maintaining robustness to variations in human behavior. In this work, we propose that robots use confidence-aware game-theoretic models of human behavior when assessing the safety of a human-robot interaction. By treating the influence between the human and robot as well as the human's rationality as unobserved latent states, we succinctly infer the degree to which a human is following the game-theoretic interaction model. We leverage this model to restrict the set of feasible human controls during safety verification, enabling the robot to confidently modulate the conservatism of its safety monitor online. Evaluations in simulated human-robot scenarios and ablation studies demonstrate that imbuing safety monitors with confidence-aware game-theoretic models enables both safe and efficient human-robot interaction. Moreover, evaluations with real traffic data show that our safety monitor is less conservative than traditional safety methods in real human driving scenarios.

关键词： automation Human-robot interaction Robustness Safety Behavioral sciences Robots Monitoring

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Failure-aware Policy Learning for Self-assessable robotics Tasks

Failure-aware Policy Learning for Self-assessable Robotics T...

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IEEE international conference on robotics and automation (ICRA)

作者： Xu, Kechun Chen, Runjian Zhao, Shuqi Li, Zizhang Yu, Hongxiang Chen, Ci Wang, Yue Xiong, Rong Zhejiang Univ Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350323658

Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the selected action evaluated as failed by the self-assessment rules, and re-choose one with the next-highest affordance (i.e. process-of-elimination strategy [1]), which ignores the dependency between the self-assessment results and the remaining untried actions. However, this dependency is important since the previous failures might help trim the remaining over-estimated actions. In this paper, we set to investigate this dependency by learning a failureaware policy. We propose two architectures for the failureaware policy by representing the self-assessment results of previous failures as the variable state, and leveraging recurrent neural networks to implicitly memorize the previous failures. Experiments conducted on three tasks demonstrate that our method can achieve better performances with higher task success rates by less trials. Moreover, when the actions are correlated, learning a failure-aware policy can achieve better performance than the process-of-elimination strategy.

关键词： Recurrent neural networks

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