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Found 6 papers, 0 papers with code
Federated reinforcement learning for robot motion planning with zero-shot generalization
This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i. e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments.
Efficient Gradient Approximation Method for Constrained Bilevel Optimization
Bilevel optimization has been developed for many machine learning tasks with large-scale and high-dimensional data.
Distributed Safe Learning and Planning for Multi-robot Systems
We propose dSLAP, a distributed safe learning and planning framework that allows the robots to safely navigate through the environments by coupling online learning and motion planning.
Learning Neural Processes on the Fly
In this paper, we propose a new algorithm called anytime neural processes that combines DNNs and SNNs and can work in both low-data and high-data regimes.
Lightweight Distributed Gaussian Process Regression for Online Machine Learning
In this paper, we study the problem where a group of agents aim to collaboratively learn a common static latent function through streaming data.
Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks
Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning.
