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arXiv 2024年

作者： Bärmann, Leonard DeChant, Chad Plewnia, Joana Peller-Konrad, Fabian Bauer, Daniel Asfour, Tamim Waibel, Alex Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Computer Science Department Columbia University NY United States

Verbalization of robot experience, i. e., summarization of and question answering about a robot’s past, is a crucial ability for improving human-robot interaction. Previous works applied rule-based systems or fine-tuned deep models to verbalize short (several-minute-long) streams of episodic data, limiting generalization and transferability. In our work, we apply large pretrained models to tackle this task with zero or few examples, and specifically focus on verbalizing lifelong experiences. For this, we derive a tree-like data structure from episodic memory (EM), with lower levels representing raw perception and proprioception data, and higher levels abstracting events to natural language concepts. Given such a hierarchical representation built from the experience stream, we apply a large language model as an agent to interactively search the EM given a user’s query, dynamically expanding (initially collapsed) tree nodes to find the relevant information. The approach keeps computational costs low even when scaling to months of robot experience data. We evaluate our method on simulated household robot data, human egocentric videos, and real-world robot recordings, demonstrating its flexibility and scalability. © 2024, CC BY.

关键词： Human robot interaction

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One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion 8

One Policy to Run Them All: an End-to-end Learning Approach ...

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8th Conference on Robot Learning, CoRL 2024

作者： Bohlinger, Nico Czechmanowski, Grzegorz Krupka, Maciej Kicki, Piotr Walas, Krzysztof Peters, Jan Tateo, Davide Department of Computer Science Technical University of Darmstadt Germany Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Research Department: Systems AI for Robot Learning Germany IDEAS NCBR Warsaw Poland Hessian.AI Germany Centre for Cognitive Science Germany

Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement Learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world. © 2024 Proceedings of Machine Learning Research.

关键词： Deep reinforcement learning

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Incentive Mechanism for Throughput Enhancement in Blockchain-based Energy Trading System

Incentive Mechanism for Throughput Enhancement in Blockchain...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei The Chinese University of Hong Kong Department of Information Engineering Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China City University of Hong Kong Department of Computer Science Hong Kong The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

ISBN: (纸本)9798350333336

In a blockchain-based energy trading system, prosumers engage in energy trading and simultaneously participate in blockchain mining to process trading transactions. However, as blockchain mining consumes energy, prosumers may lack incentives to participate, resulting in a low blockchain throughput and negatively affecting the energy trading market. To address this issue, we propose a Two-Piece Linear Compensation (TPLC) mechanism to incentivize sufficient participation for blockchain mining. We model the mechanism design as a two-stage game, wherein the system designer proposes the mechanism in Stage I, and prosumers decide on their energy allocation strategies in Stage II. We analyze the complex and coupled interactions of prosumers and uncover the reason for the low throughput issue: a self-interested prosumer neglects the positive externality of energy allocation to the blockchain, as it does not benefit him. Despite the diverse objectives of prosumers and the large search space of the mechanism, we demonstrate that the distributed TPLC mechanism achieves the maximum social welfare of a centralized decision benchmark. The numerical analysis compares the TPLC mechanism with linear compensation and no compensation benchmarks. Our results show that the TPLC mechanism achieves the highest social welfare and average payoff for prosumers. Additionally, the TPLC mechanism supports a large energy market without sacrificing the user experience. © 2023 IEEE.

关键词： Blockchain

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Resilient Consensus in Opinion Dynamics Under Adversarial Epidemics

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IFAC-PapersOnLine 2023年第2期56卷 1841-1846页

作者： Tatsuya Masada Yuan Wang Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Robotics Hunan University Changsha China

In this paper, we discuss the opinion dynamics with bounded confidence in multi-agent systems under an infection spreading environment. The dynamics of the infection spreading processes follows the so-called susceptible-infected-recovered (SIR) model. Here, the infection induces faulty behaviors in the agents whose opinions may deviate from their true opinions. Cooperating with infection suppression policies and the resilient algorithm based on the mean sub-sequence reduced (MSR) approach, resilient consensus can be attained by the regular agents within a safe region. In particular, we establish sufficient conditions for resilient consensus of opinion dynamics with large bounded confidence. A numerical example is provided to verify the effectiveness of our proposed approach.

关键词： Multi-agent systems Opinion dynamics Resilient consensus SIR epidemic model

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On the Faithfulness of Vision Transformer Explanations

arXiv

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arXiv 2024年

作者： Wu, Junyi Kang, Weitai Tang, Hao Hong, Yuan Yan, Yan Department of Computer Science Illinois Institute of Technology United States Robotics Institute Carnegie Mellon University United States Department of Computer Science University of Connecticut United States

To interpret Vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps. However, whether these interpretations reflect true rationales behind the model’s output is still underexplored. To address this gap, we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model’s predictions. To evaluate faithfulness, we introduce Salience-guided Faithfulness Coefficient (SaCo), a novel evaluation metric leveraging essential information of salience distribution. Specifically, we conduct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores, resulting in a coefficient that indicates the explanation’s degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution, thereby failing to capture the faithfulness property. In contrast, our proposed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations. Furthermore, our SaCo demonstrates that the use of gradient and multilayer aggregation can markedly enhance the faithfulness of attention-based explanation, shedding light on potential paths for advancing Vision Transformer explainability. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution transformers

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Simulation of a Differential Robot Drive in Gazebo

Simulation of a Differential Robot Drive in Gazebo

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2023 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2023

作者： Tseeva, Fatimat M. Shogenova, Mariyana M. Senov, Khamisha M. Nogerov, Ibragim A. Bozieva, Asiyat M. Institute of Computer Science Electronics and Robotics Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University named after H.M. Berbekov Nalchik Russia

ISBN: (纸本)9798350340532

A differential drive is one of the most common drives in robotics that drives a robot by rotating two wheels at different speeds. This article discusses the features of modeling a robot differential drive in the Gazebo simulator. In particular, the key challenges that designers face when modeling differential drive, such as motion control and wheel inertia modeling, will be presented, as well as solutions that can be applied to achieve the desired results. The article will also describe how to use ROS (Robot Operating System) to simulate differential drive in Gazebo. ROS provides tools for controlling a robot in a simulation and provides a convenient way to exchange data between the components of a robot system. Finally, several examples will be presented showing how Gazebo can be used to simulate various types of differential drive robots, including wheeled robots and mobile robots with wide tracks. These examples demonstrate how proper simulation of a differential drive can be useful for developing and testing robot control algorithms in simulation, which can greatly speed up the development process and reduce the cost of experimental work. © 2023 IEEE.

关键词： Robot Operating System

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Transformers for Image-Goal Navigation

arXiv

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arXiv 2024年

作者： Pelluri, Nikhilanj V. Minnesota Robotics Institute Department of Computer Science and Engineering University of Minnesota Twin Cities United States

Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specified by an image, relying only on images from an onboard camera. This task is particularly challenging since it demands robust scene understanding, goal-oriented planning and long-horizon navigation. Most existing approaches typically learn navigation policies reliant on recurrent neural networks trained via online reinforcement learning. However, training such policies requires substantial computational resources and time, and performance of these models is not reliable on long-horizon navigation. In this work, we present a generative Transformer based model that jointly models image goals, camera observations and the robot’s past actions to predict future actions. We use state-of-the-art perception models and navigation policies to learn robust goal conditioned policies without the need for real-time interaction with the environment. Our model demonstrates capability in capturing and associating visual information across long time horizons, helping in effective navigation. © 2024, CC BY.

关键词： Navigation

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Formal Modelling for Multi-Robot Systems Under Uncertainty

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Current robotics Reports 2023年第3期4卷 55-64页

作者： Street, Charlie Mansouri, Masoumeh Lacerda, Bruno School of Computer Science University of Birmingham Birmingham UK Oxford Robotics Institute University of Oxford Oxford UK

To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, we review modelling formalisms for multi-robot systems under uncertainty and discuss how they can be used for planning, reinforcement learning, model checking, and simulation. Recent work has investigated models which more accurately capture multi-robot execution by considering different forms of uncertainty, such as temporal uncertainty and partial observability, and modelling the effects of robot interactions on action execution. Other strands of work have presented approaches for reducing the size of multi-robot models to admit more efficient solution methods. This can be achieved by decoupling the robots under independence assumptions or reasoning over higher-level macro actions. Existing multi-robot models demonstrate a trade-off between accurately capturing robot dependencies and uncertainty, and being small enough to tractably solve real-world problems. Therefore, future research should exploit realistic assumptions over multi-robot behaviour to develop smaller models which retain accurate representations of uncertainty and robot interactions; and exploit the structure of multi-robot problems, such as factored state spaces, to develop scalable solution methods.

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Autonomous Sensor Exchange and Calibration for Cornstalk Nitrate Monitoring Robot

arXiv

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arXiv 2024年

作者： Lee, Janice Seungyeon Detlefsen, Thomas Lawande, Shara Ghatge, Saudamini Shanthi, Shrudhi Ramesh Mukkamala, Sruthi Kantor, George Kroemer, Oliver Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

Interactive sensors are an important component of robotic systems but often require manual replacement due to wear and tear. Automating this process can enhance system autonomy and facilitate long-term deployment. We developed an autonomous sensor exchange and calibration system for an agriculture crop monitoring robot that inserts a nitrate sensor into cornstalks. A novel gripper and replacement mechanism, featuring a reliable funneling design, were developed to enable efficient and reliable sensor exchanges. To maintain consistent nitrate sensor measurement, an on-board sensor calibration station was integrated to provide in-field sensor cleaning and calibration. The system was deployed at the Ames Curtis Farm in June 2024, where it successfully inserted nitrate sensors with high accuracy into 30 cornstalks with a 77% success rate. © 2024, CC BY-SA.

关键词： Agricultural robots

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Nonlinear Model Predictive Control for Optimal Dose Administration in Radiotherapy 15th

Nonlinear Model Predictive Control for Optimal Dose Administ...

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15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024

作者： Araújo, João C. G. Guerreiro, Bruno J. Oliveira, Luis B. da Silva, Filipe Ferreira CTS-UNINOVA and LASI Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica2829-516 Portugal Institute for Systems and Robotics LARSyS Lisbon1049-001 Portugal CEFITEC Department of Physics NOVA School of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9783031638503

This work discusses the nonlinear modeling of healthy and cancer cell population growth under the influence of radiotherapy, and proposes predictive control techniques to determine the optimal dose for tumor control, while sparing surrounding healthy tissue. Radiation effects are modelled by a linear-quadratic formalism, coupled with a novel population-specific delayed dose response. Numerical simulations of the open- and closed-loop system are conducted, and robustness against model uncertainty is studied. Results illustrate the controller’s performance when dealing with imperfect information and unexpected system behavior, and how it can be tuned to produce the desired treatment outcome. However, optimal operation is conditioned by a limited prediction horizon, as well as strict adherence to treatment schedules and dose restrictions. © IFIP International Federation for Information Processing 2024.

关键词： Nonlinear systems

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