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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Elias Marks Jonas Bömer Federico Magistri Anurag Sag Jens Behley Cyrill Stachniss Center for Robotics University of Bonn Germany Institute of Sugar Beet Research Göttingen Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Agricultural production is facing challenges in the next decades induced by climate change and the need for more sustainability by reducing its impact on the environment. Advances in field management through robotic intervention, monitoring of crops by autonomous unmanned aerial vehicles (UAVs) supporting breeding of novel and more resilient crop varieties can help to address these challenges. The analysis of plant traits is called phenotyping and is an essential activity in plant breeding; it however involves a great amount of manual labor. With this paper, we provide means to better tackle the problems of instance segmentation to support robotic intervention and automatic fine-grained, organ-level geometric analysis needed for precision phenotyping. As the availability of real-world data in this domain is relatively scarce, we provide a novel dataset that was acquired using UAVs capturing high-resolution images of real breeding trials containing 48 plant varieties and therefore covering a relevant morphological and appearance spectrum. This enables the development of approaches for instance segmentation and autonomous phenotyping that generalize well to different plant varieties. Based on overlapping high-resolution images taken from multiple viewing angles, we provide photogrammetric dense point clouds and provide detailed and accurate point-wise labels for plants, leaves, and salient points as the tip and the base in 3D. Additionally, we include measurements of phenotypic traits performed by experts from the German Federal Plant Variety Office on the real plants, allowing the evaluation of new approaches not only on segmentation and keypoint detection but also directly on actual traits. The provided labeled point clouds enable finegrained plant analysis and support further progress in the development of automatic phenotyping approaches, but also enable further research in surface reconstruction, point cloud completion, and semantic interpretation of point clouds

关键词： Point cloud compression Instance segmentation Surface reconstruction Three-dimensional displays Semantics Crops Surface morphology Production Sugar industry Sustainable development

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Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation

Leveraging GNSS and Onboard Visual Data from Consumer Vehicl...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Balázs Opra Betty Le Dem Jeffrey M. Walls Dimitar Lukarski Cyrill Stachniss Woven by Toyota Inc University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road graph remains labor-intensive and has yet to achieve full automation. The goal of this paper is to generate such graphs automatically and accurately. Modern cars are equipped with onboard sensors used for today's advanced driver assistance systems like lane keeping. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles to estimate road-level maps with minimal effort. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network. We also utilize the data’s time series nature to refine the neural network’s output by using map matching. We implemented and evaluated our method using a fleet of real consumer vehicles, only using the deployed onboard sensors. Our evaluation demonstrates that our approach not only matches existing methods on simpler road configurations but also significantly outperforms them on more complex road geometries and topologies. This work received the 2023 Woven by Toyota Invention Award.

关键词： Global navigation satellite system Technological innovation Visualization Accuracy Roads Weaving Sensor systems Sensors Automobiles Vehicles

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FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features

arXiv

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arXiv 2024年

作者： Rochow, Andre Schwarz, Max Behnke, Sven Autonomous Intelligent Systems - Computer Science Institute VI and Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image, which may be of a different person (cross-reenactment). Most existing methods are CNN-based and estimate optical flow from the source image to the current driving frame, which is then inpainted and refined to produce the output animation. We propose a transformer-based encoder for computing a set-latent representation of the source image(s). We then predict the output color of a query pixel using a transformer-based decoder, which is conditioned with keypoints and a facial expression vector extracted from the driving frame. Latent representations of the source person are learned in a self-supervised manner that factorize their appearance, head pose, and facial expressions. Thus, they are perfectly suited for cross-reenactment. In contrast to most related work, our method naturally extends to multiple source images and can thus adapt to person-specific facial dynamics. We also propose data augmentation and regularization schemes that are necessary to prevent overfitting and support generalizability of the learned representations. We evaluated our approach in a randomized user study. The results indicate superior performance compared to the state-of-the-art in terms of motion transfer quality and temporal consistency.1 Copyright © 2024, The Authors. All rights reserved.

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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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Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps

arXiv

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arXiv 2025年

作者： Gupta, Saurabh Guadagnino, Tiziano Mersch, Benedikt Trekel, Niklas Malladi, Meher V.R. Stachniss, Cyrill Center for Robotics University of Bonn Germany Department of Engineering Science University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Consistent maps are key for most autonomous mobile robots. They often use SLAM approaches to build such maps. Loop closures via place recognition help maintain accurate pose estimates by mitigating global drift. This paper presents a robust loop closure detection pipeline for outdoor SLAM with LiDAR-equipped robots. The method handles various LiDAR sensors with different scanning patterns, field of views and resolutions. It generates local maps from LiDAR scans and aligns them using a ground alignment module to handle both planar and non-planar motion of the LiDAR, ensuring applicability across platforms. The method uses density-preserving bird’s eye view projections of these local maps and extracts ORB feature descriptors from them for place recognition. It stores the feature descriptors in a binary search tree for efficient retrieval, and self-similarity pruning addresses perceptual aliasing in repetitive environments. Extensive experiments on public and self-recorded datasets demonstrate accurate loop closure detection, long-term localization, and cross-platform multi-map alignment, agnostic to the LiDAR scanning patterns, fields of view, and motion profiles. Copyright © 2025, The Authors. All rights reserved.

关键词： SLAM robotics

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VARIATIONAL AUTOENCODERS IN THE PRESENCE OF LOW-DIMENSIONAL DATA: LANDSCAPE AND IMPLICIT BIAS 10

VARIATIONAL AUTOENCODERS IN THE PRESENCE OF LOW-DIMENSIONAL ...

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10th International Conference on learning Representations, ICLR 2022

作者： Koehler, Frederic Mehta, Viraj Zhou, Chenghui Risteski, Andrej Department of Computer Science Stanford University United States Robotics Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

Variational Autoencoders (VAEs) are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lower dimensional manifold. Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture that in standard VAE training the generator will converge to a solution with 0 variance which is correctly supported on the ground truth manifold. They gave partial support for this conjecture by showing that some optima of the VAE loss do satisfy this property, but did not analyze the training dynamics. In this paper, we show that for linear encoders/decoders, the conjecture is true-that is the VAE training does recover a generator with support equal to the ground truth manifold-and does so due to an implicit bias of gradient descent rather than merely the VAE loss itself. In the nonlinear case, we show that VAE training frequently learns a higher-dimensional manifold which is a superset of the ground truth manifold. © 2022 ICLR 2022 - 10th International Conference on learning Representationss. All rights reserved.

关键词： Gradient methods

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Safe Multi-Agent Reinforcement learning for Behavior-Based Cooperative Navigation

arXiv

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arXiv 2023年

作者： Dawood, Murad Pan, Sicong Dengler, Nils Zhou, Siqi Schoellig, Angela P. Bennewitz, Maren The Humanoid Robots Lab University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence and the Center for Robotics Bonn Germany The Learning Systems and Robotics lab The Technical University of Munich Germany

In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning (MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training. © 2023, CC0.

关键词： Adversarial machine learning

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LiLMaps: Learnable Implicit Language Maps

arXiv

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arXiv 2025年

作者： Kruzhkov, Evgenii Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI University of Bonn Germany Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics and The Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

One of the current trends in robotics is to employ large language models (LLMs) to provide non-predefined command execution and natural human-robot interaction. It is useful to have an environment map together with its language representation, which can be further utilized by LLMs. Such a comprehensive scene representation enables numerous ways of interaction with the map for autonomously operating robots. In this work, we present an approach that enhances incremental implicit mapping through the integration of vision-language features. Specifically, we (i) propose a decoder optimization technique for implicit language maps which can be used when new objects appear on the scene, and (ii) address the problem of inconsistent vision-language predictions between different viewing positions. Our experiments demonstrate the effectiveness of LiLMaps and solid improvements in performance. © 2025, CC BY-NC-ND.

关键词： Visual languages

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PID-Inspired Inductive Biases for Deep Reinforcement learning in Partially Observable Control Tasks

arXiv

引用

arXiv 2023年

作者： Char, Ian Schneider, Jeff Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the system is often not observable. When this is the case, the policy needs to leverage the history of observations to infer the current state. At the same time, differences between the training and testing environments makes it critical for the policy not to overfit to the sequence of observations it sees at training time. As such, there is an important balancing act between having the history encoder be flexible enough to extract relevant information, yet be robust to changes in the environment. To strike this balance, we look to the PID controller for inspiration. We assert the PID controller’s success shows that only summing and differencing are needed to accumulate information over time for many control tasks. Following this principle, we propose two architectures for encoding history: one that directly uses PID features and another that extends these core ideas and can be used in arbitrary control tasks. When compared with prior approaches, our encoders produce policies that are often more robust and achieve better performance on a variety of tracking tasks. Going beyond tracking tasks, our policies achieve 1.7x better performance on average over previous state-of-the-art methods on a suite of locomotion control tasks. © 2023, CC BY.

关键词： Reinforcement learning

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Predicting against the Flow: Boosting Source Localization by Means of Field Belief Modeling using Upstream Source Proximity

Predicting against the Flow: Boosting Source Localization by...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Finn L Busch Nathalie Bauschmann Sami Haddadin Robert Seifried Daniel A Duecker Institute of Mechanics and Ocean Engineering Hamburg University of Technology Germany Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Sweden Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Time-effective and accurate source localization with mobile robots is crucial in safety-critical scenarios, e.g. leakage detection. This becomes particular challenging in realistic cluttered scenarios, i.e. in the presence of complex current flows or wind. Traditional methods often fall short due to simplifications or limited onboard *** propose to combine source localization with a Gaussian Markov Random Field (GMRF). This allows to improve source localization hypotheses by building on the GMRF’s concentration and flow field belief that are continuously updated by gathered measurements. We introduce the upstream source proximity (USP) as a natural metric that exploits the joint knowledge represented in the field belief’s concentration and flow field, i.e. predicting sources upstream. As a result, our method yields a computationally efficient source localization and field belief module providing substantially more stable gradients than conventional concentration gradient-based *** demonstrate the suitability of our approach in a series of numerical experiments covering complex source location scenarios. With regard to computational requirements, the method achieves update rates of 10Hz on a RaspberryPi4B.

关键词： Location awareness Measurement Wind Computational modeling Position measurement Predictive models Probabilistic logic

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