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Sustainable Grid through Distributed Data Centers Spinning AI Demand for Grid Stabilization and Optimization

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arXiv 2025年

作者： Evans, Scott C. Dahlin, Nathan Ndiaye, Ibrahima Ekanayake, Sachini Piyoni Duncan, Alexander Rose, Blake Huang, Hao AI-Machine Learning Robotics Lab United States Electrification Mission United States ECE Department University at Albany SUNY United States

We propose a disruptive paradigm to actively place and schedule TWhrs of parallel AI jobs strategically on the grid, at distributed, grid-aware high performance compute data centers (HPC) capable of using their massive power and energy load to stabilize the grid while reducing grid build-out requirements, maximizing use of renewable energy, and reducing Green House Gas (GHG) emissions. Our approach will enable the creation of new, value adding markets for spinning compute demand, providing market based incentives that will drive the joint optimization of energy and learning. © 2025, CC BY.

关键词： Stabilization

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

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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

Latent Action Priors for Locomotion with Deep Reinforcement learning

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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

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

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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

Relative Representations: Topological and Geometric Perspectives

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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

Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories

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arXiv 2024年

作者： Wiesmann, Louis Marks, Elias Gupta, Saurabh Guadagnino, Tiziano Behley, Jens Stachniss, Cyrill The Center for Robotics University of Bonn Germany The Department of Engineering Science The University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from mobile 3D LiDAR sensors is often used. Mapping requires correctly aligning the individual point clouds to each other to obtain a globally consistent map. In this paper, we investigate the problem of multi-scan alignment to obtain globally consistent point cloud maps. We propose a 3D LiDAR bundle adjustment approach to solve the global alignment problem and jointly optimize the available data. Utilizing a continuous-time trajectory allows us to consider the ego-motion of the LiDAR scanner while recording a single scan directly in the least squares adjustment. Furthermore, pruning the search space of correspondences and utilizing out-of-core circular buffer enables our approach to align thousands of point clouds efficiently. We successfully align point clouds recorded with a handheld LiDAR, as well as ones mounted on a vehicle, and are able to perform multi-session alignment. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

A Dataset and Benchmark for Shape Completion of Fruits for Agricultural robotics

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arXiv 2024年

作者： Magistri, Federico Läbe, Thomas Marks, Elias Nagulavancha, Sumanth Pan, Yue Smitt, Claus Klingbeil, Lasse Halstead, Michael Kuhlmann, Heiner McCool, Chris Behley, Jens Stachniss, Cyrill The Center for Robotics The University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany The Department of Engineering Science The University of Oxford United Kingdom

As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-intensive manual tasks like fruit picking. To be effective, such systems need to monitor and interact with plants and fruits precisely, which is challenging due to the cluttered nature of agricultural environments causing, for example, strong occlusions. Thus, being able to estimate the complete 3D shapes of objects in presence of occlusions is crucial for automating operations such as fruit harvesting. In this paper, we propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits. Specifically, our dataset contains RGB-D frames of single sweet peppers in lab conditions but also in a commercial greenhouse. For each fruit, we additionally collected high-precision point clouds that we use as ground truth. For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits. We release our dataset, consisting of almost 7,000 RGB-D frames belonging to more than 100 different fruits. We provide segmented RGB-D frames, with camera intrinsics to easily obtain colored point clouds, together with the corresponding high-precision, occlusion-free point clouds obtained with a high-precision laser scanner. We additionally enable evaluation of shape completion approaches on a hidden test set through a public challenge on a benchmark server. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition

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TReR: A Lightweight Transformer Re-Ranking Approach for 3D L...

International Conference on Intelligent Transportation

作者： Tiago Barros Luís Garrote Martin Aleksandrov Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Portugal Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin

Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. However, when deployed in challenging real-world scenarios, the place recognition models become more complex, which comes at the cost of high computational demand. This work tackles this problem from an information-retrieval perspective, adopting a first-retrieve-then-re-ranking paradigm, where an initial loop candidate ranking, generated from a 3D place recognition model, is re-ordered by a proposed lightweight transformer-based re-ranking approach (TReR). The proposed approach relies on global descriptors only, being agnostic to the place recognition model. The experimental evaluation, conducted on the KITTI Odometry dataset, where we compared TReR with s.o.t.a. re-ranking approaches such as αQE and SGV, indicate the robustness and efficiency when compared to αQE while offering a good trade-off between robustness and efficiency when compared to SGV.

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TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition

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arXiv 2023年

作者： Barros, Tiago Garrote, Luís Aleksandrov, Martin Premebida, Cristiano Nunes, Urbano J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin Germany

关键词： Efficiency