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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jorge De Heuvel Florian Seiler Maren Bennewitz Humanoid Robots Lab and the Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, we introduce EnQuery, a query generation approach using an ensemble of policies that achieve behavioral diversity through a regularization term. For a given navigation task, EnQuery produces multiple navigation trajectory suggestions, thereby optimizing the efficiency of preference data collection with fewer queries. Our methodology demonstrates superior performance in aligning navigation policies with user preferences in low-query regimes, offering enhanced policy convergence from sparse preference queries. The evaluation is complemented with a novel explainability representation, capturing full scene navigation behavior of the mobile robot in a single plot. Our code is available online at https://***/hrl-bonn/EnQuery.

关键词： Training Navigation Data visualization Reinforcement learning Reliability engineering Trajectory Behavioral sciences Recycling Mobile robots Data mining

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Simulation and HRI recent perspectives with the MORSE simulator

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8810卷 13-24页

作者： Lemaignan, Séverin Hanheide, Marc Karg, Michael Khambhaita, Harmish Kunze, Lars Lier, Florian Lütkebohle, Ingo Milliez, Grégoire CHILI Lab. EPFL Lausanne Switzerland Centre for Autonomous Systems University of Lincoln United Kingdom IAS Technische Universität München Germany LAAS/CNRS Université de Toulouse France Intelligent Robotics Lab. University of Birmingham United Kingdom CITEC Bielefeld University Germany Machine Learning and Robotics Lab. Universität Stuttgart Germany

Simulation in robotics is often a love-hate relationship: while simulators do save us a lot of time and effort compared to regular deployment of complex software architectures on complex hardware, simulators are also known to evade many of the real issues that robots need to manage when they enter the real world. Because humans are the paragon of dynamic, unpredictable, complex, real world entities, simulation of human-robot interactions may look condemn to fail, or, in the best case, to be mostly useless. This collective article reports on five independent applications of the MORSE simulator in the field of human-robot interaction: It appears that simulation is already useful, if not essential, to successfully carry out research in the field of HRI, and sometimes in scenarios we do not anticipate. © 2014 Springer International Publishing Switzerland.

关键词： Simulators

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Control-Barrier-Aided Teleoperation with Visual-Inertial SLAM for Safe MAV Navigation in Complex Environments

Control-Barrier-Aided Teleoperation with Visual-Inertial SLA...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Siqi Zhou Sotiris Papatheodorou Stefan Leutenegger Angela P. Schoellig Learning Systems and Robotics Lab School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence (MIRMI) Smart Robotics Lab School of Computation Information and Technology Technical University of Munich Department of Computing Smart Robotics Lab Imperial College London

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we consider a Micro Aerial Vehicle (MAV) system teleoperated by a non-expert and introduce a perceptive safety filter that leverages Control Barrier Functions (CBFs) in conjunction with Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) and dense 3D occupancy mapping to guarantee safe navigation in complex and unstructured environments. Our system relies solely on onboard IMU measurements, stereo infrared images, and depth images and autonomously corrects teleoperated inputs when they are deemed unsafe. We define a point in 3D space as unsafe if it satisfies either of two conditions: (i) it is occupied by an obstacle, or (ii) it remains unmapped. At each time step, an occupancy map of the environment is updated by the VI-SLAM by fusing the onboard measurements, and a CBF is constructed to parameterize the (un)safe region in the 3D space. Given the CBF and state feedback from the VI-SLAM module, a safety filter computes a certified reference that best matches the teleoperation input while satisfying the safety constraint encoded by the CBF. In contrast to existing perception-based safe control frameworks, we directly close the perception-action loop and demonstrate the full capability of safe control in combination with real-time VI-SLAM without any external infrastructure or prior knowledge of the environment. We verify the efficacy of the perceptive safety filter in real-time MAV experiments using exclusively onboard sensing and computation and show that the teleoperated MAV is able to safely navigate through unknown environments despite arbitrary inputs sent by the teleoperator.

关键词： Teleoperators State feedback Three-dimensional displays Simultaneous localization and mapping Navigation Real-time systems Time measurement

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Latent Action Priors for Locomotion with Deep Reinforcement learning

arXiv

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arXiv 2024年

作者： Hausdörfer, Oliver von Rohr, Alexander Lefort, Éric Schoellig, Angela P. The Technical University of Munich Germany TUM School of Computation Information and Technology Department of Computer Engineering Learning Systems and Robotics Lab Germany Munich Institute of Robotics and Machine Intelligence Germany

Deep Reinforcement learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-level torque control, as inductive biases are difficult to integrate into the learning process. We propose an inductive bias for learning locomotion that is especially useful for torque control: latent actions learned from a small dataset of expert demonstrations. This prior allows the policy to directly leverage knowledge contained in the expert’s actions and facilitates more efficient exploration. We observe that the agent is not restricted to the reward levels of the demonstration, and performance in transfer tasks is improved significantly. Latent action priors combined with style rewards for imitation leads to a closer replication of the expert’s behavior. Videos and code are available at https://***/view/ latent-action-priors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Relative Representations: Topological and Geometric Perspectives

arXiv

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arXiv 2024年

作者： García-Castellanos, Alejandro Marchetti, Giovanni Luca Kragic, Danica Scolamiero, Martina Amsterdam Machine Learning Lab University of Amsterdam Netherlands Department of Mathematics KTH Royal Institute of Technology Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching. © 2024, CC BY.

关键词： Topology

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Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador

arXiv

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arXiv 2017年

作者： De-Arteaga, Maria Dubrawski, Artur Auton Lab Carnegie Mellon University Machine Learning Department Carnegie Mellon University Heinz College Carnegie Mellon University Robotics Institute Carnegie Mellon University

When sexual violence is a product of organized crime or social imaginary, the links between sexual violence episodes can be understood as a latent structure. With this assumption in place, we can use data science to uncover complex patterns. In this paper we focus on the use of data mining techniques to unveil complex anomalous spatiotemporal patterns of sexual violence. We illustrate their use by analyzing all reported rapes in El Salvador over a period of nine years. Through our analysis, we are able to provide evidence of phenomena that, to the best of our knowledge, have not been previously reported in literature. We devote special attention to a pattern we discover in the East, where underage victims report their boyfriends as perpetrators at anomalously high rates. Finally, we explain how such analyzes could be conducted in real-time, enabling early detection of emerging patterns to allow law enforcement agencies and policy makers to react accordingly. Copyright © 2017, The Authors. All rights reserved.

关键词： Anomaly detection

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Target-Aware Implicit Mapping for Agricultural Crop Inspection

Target-Aware Implicit Mapping for Agricultural Crop Inspecti...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shane Kelly Alessandro Riccardi Elias Marks Federico Magistri Tiziano Guadagnino Margarita Chli Cyrill Stachniss Vision for Robotics Lab ETH Zurich University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany Department of Engineering Science University of Oxford UK

Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural environments to create dense maps of crop rows with high enough fidelity to be useful for automated crop inspection. Specifically, we map strawberry and sweet pepper crop rows using RGB images captured by a wheeled mobile field robot inside a greenhouse and then use this data to build 3D maps to document the development of plants and fruits. Our Target-Aware Implicit Mapping system (TAIM) uses a SLAM-based pose initialization strategy for robust pose convergence, an efficient information-guided training sample selection framework for faster loss reduction, and focuses on exploiting training samples for fruit regions of the scene, which are critical for crop inspection tasks, to create more accurate maps in less time.

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Radar-Only Odometry and Mapping for Autonomous Vehicles

Radar-Only Odometry and Mapping for Autonomous Vehicles

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Casado Herraez Matthias Zeller Le Chang Ignacio Vizzo Michael Heidingsfeld Cyrill Stachniss CARIAD SE and With the University of Bonn Germany CARIAD SE and With the University of Stuttgart Germany Dexory UK University of Bonn is With CARIAD SE Germany Center for Robotics University of Bonn and With the Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP approach that leverages the velocity information provided by 3D radar sensors. The second one is advantageous for 2D radars with a low number of samples, and particularly useful for scenarios where the sensor is being blocked by large dynamic obstacles. It exploits a constant velocity filter and the measured Doppler velocities to estimate the vehicle’s ego-motion. We enrich this with a filtering step to improve the accuracy of the points in the resulting map. We put our work to the test using the View of Delft and NuScenes datasets, which involve 3D and 2D radar sensors. Our findings illustrate state-of-the-art performance of our odometry techniques in terms of accuracy when compared to existing alternatives. Moreover, we demonstrate that our map filtering methodology achieves higher similarity rates than the raw unfiltered map when benchmarked against a corresponding LiDAR map.

关键词： Three-dimensional displays Accuracy Laser radar Radar measurements Navigation Doppler radar Sensors

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Balanced resonate-and-fire neurons 24

Balanced resonate-and-fire neurons

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Proceedings of the 41st International Conference on machine learning

作者： Saya Higuchi Sebastian Kairat Sander M. Bohté Sebastian Otte Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany Machine Learning Group Centrum Wiskunde & Informatica (CWI) Amsterdam The Netherlands

The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.

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Deep learning Approach for Linear Locomotion Control of Spherical Robot

Deep Learning Approach for Linear Locomotion Control of Sphe...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Daniel W Nam Chaehyeuk Lee Jaehwan Choi Yeonjun Kim Soon-Geul Lee KC Machine Learning Lab (ML2) Seoul Korea Medipixel Seoul Korea HANCOM Robotics Pangyo Korea Kyung Hee University Yongin Korea

Spherical robot is a typical nonlinear underactuated nonholonomic system that is difficult to control accurate path trajectory and uniform and stable posture during transfer. In this paper, we propose a method called controller guided model learning (CGML) to control its posture and trajectory path simultaneously. This model-based control algorithm is a combination of traditional classical control techniques and model learning through deep neural networks. CGML implemented with artificial neural network improves data efficiency in learning by using reliability advantage of classical controller. The proposed method can reduce the learning time required for convergence, which is suitable for direct online learning. The simulation environment experiment of the spherical robot showed that CGML is superior to other algorithms in terms of data efficiency.

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