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Related papers: Deep Kernels for Optimizing Locomotion Controllers

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Stochastic Gradient Descent (SGD) has proven to be remarkably effective in optimizing deep neural networks that employ ever-larger numbers of parameters. Yet, improving the efficiency of large-scale optimization remains a vital and highly…

Machine Learning · Computer Science 2020-11-11 Frithjof Gressmann , Zach Eaton-Rosen , Carlo Luschi

Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the resulting driving style. However, finding a suitable cost function…

Systems and Control · Electrical Eng. & Systems 2025-10-20 Lukas Theiner , Sebastian Hirt , Alexander Steinke , Rolf Findeisen

Machine learning models have achieved human-level performance on various tasks. This success comes at a high cost of computation and storage overhead, which makes machine learning algorithms difficult to deploy on edge devices. Typically,…

Machine Learning · Computer Science 2021-11-12 Giuseppina Carannante , Dimah Dera , Ghulam Rasool , Nidhal C. Bouaynaya

Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional…

Machine Learning · Computer Science 2020-01-27 Byung Hoon Ahn , Prannoy Pilligundla , Amir Yazdanbakhsh , Hadi Esmaeilzadeh

We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework. In particular, we learn a distance metric through a deep neural network. Through this metric, we project the…

Machine Learning · Computer Science 2020-05-13 Selim F. Yilmaz , Suleyman S. Kozat

Accurate positioning and fast traversal times determine the productivity in machining applications. This paper demonstrates a hierarchical contour control implementation for the increase of productivity in positioning systems. The…

Systems and Control · Electrical Eng. & Systems 2024-04-30 Alisa Rupenyan , Mohammad Khosravi , John Lygeros

Self-driving laboratories (SDLs) are combining recent technological advances in robotics, automation, and machine learning based data analysis and decision-making to perform autonomous experimentation toward human-directed goals without…

Machine Learning · Computer Science 2025-12-18 Luca Torresi , Pascal Friederich

Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that improves sample-efficiency and generalizability of locomotion…

Robotics · Computer Science 2019-09-30 Tianyu Li , Nathan Lambert , Roberto Calandra , Franziska Meier , Akshara Rai

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as…

Machine Learning · Computer Science 2012-03-19 Kaizhu Huang , Rong Jin , Zenglin Xu , Cheng-Lin Liu

Sampling-based methods for motion planning, which capture the structure of the robot's free space via (typically random) sampling, have gained popularity due to their scalability, simplicity, and for offering global guarantees, such as…

Robotics · Computer Science 2025-05-22 Itai Panasoff , Kiril Solovey

Robotic perception models, such as Deep Neural Networks (DNNs), are becoming more computationally intensive and there are several models being trained with accuracy and latency trade-offs. However, modern latency accuracy trade-offs largely…

Robotics · Computer Science 2022-07-14 Bineet Ghosh , Masaad Khan , Adithya Ashok , Sandeep Chinchali , Parasara Sridhar Duggirala

Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can…

A properly designed controller can help improve the quality of experimental measurements or force a dynamical system to follow a completely new time-evolution path. Recent developments in deep reinforcement learning have made steep advances…

Statistical Mechanics · Physics 2025-02-26 Ruslan Mukhamadiarov

Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this work, we propose a practical and theoretically-justified approach…

Robotics · Computer Science 2022-02-17 Thomas Lew , Apoorva Sharma , James Harrison , Andrew Bylard , Marco Pavone

Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting inaccuracies of the learned model. We propose to regularize…

Machine Learning · Computer Science 2019-12-30 Rinu Boney , Norman Di Palo , Mathias Berglund , Alexander Ilin , Juho Kannala , Antti Rasmus , Harri Valpola

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of…

Machine Learning · Statistics 2022-06-03 Jingyi Zhang , Cheng Meng , Jun Yu , Mengrui Zhang , Wenxuan Zhong , Ping Ma

Achieving ultimate bounds in estimation processes is the main objective of quantum metrology. In this context, several problems require measurement of multiple parameters by employing only a limited amount of resources. To this end,…

Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Consequently, they can often be compressed using techniques such…

Machine Learning · Computer Science 2020-12-03 Vinu Joseph , Saurav Muralidharan , Animesh Garg , Michael Garland , Ganesh Gopalakrishnan

One's ability to learn a generative model of the world without supervision depends on the extent to which one can construct abstract knowledge representations that generalize across experiences. To this end, capturing an accurate…

Machine Learning · Computer Science 2021-10-28 Zahra Sheikhbahaee , Dongshu Luo , Blake VanBerlo , S. Alex Yun , Adam Safron , Jesse Hoey

Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll-outs result in failure behaviors, causing premature…

Machine Learning · Computer Science 2020-11-11 Alonso Marco , Dominik Baumann , Philipp Hennig , Sebastian Trimpe