Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation

• URL: http://arxiv.org/abs/2411.04112v1
• Date: Wed, 06 Nov 2024 18:44:09 GMT
• Title: Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation
• Authors: Shreya Gummadi, Mateus V. Gasparino, Deepak Vasisht, Girish Chowdhary,
• Abstract summary: Federated-EmbedCluster (Fed-EC) is a clustering-based federated learning framework deployed with vision based autonomous robot navigation in diverse outdoor environments. Fed-EC reduces the communication size by 23x for each robot while matching the performance of centralized learning for goal-oriented navigation and outperforms local learning.
• Score: 7.8839937556789375
• License:
• Abstract: Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning methods deployed in robotics aim to learn a single global model across robots that works ideally for all. But in practice one model may not be well suited for robots deployed in various environments. This paper proposes Federated-EmbedCluster (Fed-EC), a clustering-based federated learning framework that is deployed with vision based autonomous robot navigation in diverse outdoor environments. The framework addresses the key federated learning challenge of deteriorating model performance of a single global model due to the presence of non-IID data across real-world robots. Extensive real-world experiments validate that Fed-EC reduces the communication size by 23x for each robot while matching the performance of centralized learning for goal-oriented navigation and outperforms local learning. Fed-EC can transfer previously learnt models to new robots that join the cluster.

Related papers

• Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments [26.66666135624716]
  We present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies. RUMs can generalize to new environments without any finetuning. We train five utility models for opening cabinet doors, opening drawers, picking up napkins, picking up paper bags, and reorienting fallen objects.
  arXiv  Detail & Related papers  (2024-09-09T17:59:50Z)
• CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering [12.472038137777474]
  Human Activity Recognition (HAR) is vital for the automation and intelligent identification of human actions through data from diverse sensors. Traditional machine learning approaches by aggregating data on a central server and centralized processing are memory-intensive and raise privacy concerns. This work proposes CDFL, an efficient federated learning framework for image-based HAR.
  arXiv  Detail & Related papers  (2024-07-17T03:17:53Z)
• AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents [109.3804962220498]
  AutoRT is a system to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision. We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies. We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
  arXiv  Detail & Related papers  (2024-01-23T18:45:54Z)
• Robot Fleet Learning via Policy Merging [58.5086287737653]
  We propose FLEET-MERGE to efficiently merge policies in the fleet setting. We show that FLEET-MERGE consolidates the behavior of policies trained on 50 tasks in the Meta-World environment. We introduce a novel robotic tool-use benchmark, FLEET-TOOLS, for fleet policy learning in compositional and contact-rich robot manipulation tasks.
  arXiv  Detail & Related papers  (2023-10-02T17:23:51Z)
• RT-1: Robotics Transformer for Real-World Control at Scale [98.09428483862165]
  We present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks.
  arXiv  Detail & Related papers  (2022-12-13T18:55:15Z)
• GNM: A General Navigation Model to Drive Any Robot [67.40225397212717]
  General goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots. We analyze the necessary design decisions for effective data sharing across robots. We deploy the trained GNM on a range of new robots, including an under quadrotor.
  arXiv  Detail & Related papers  (2022-10-07T07:26:41Z)
• On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios [0.0]
  Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices. Mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL) Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables.
  arXiv  Detail & Related papers  (2022-07-19T16:17:12Z)
• Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection [101.38634057635373]
  We focus on applications where robots are required to jointly select actions to maximize team submodular objectives. We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
  arXiv  Detail & Related papers  (2021-05-18T15:32:07Z)
• Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning [5.478764356647437]
  A novel cognitive architecture for multi-agent LfD robotic learning is introduced, targeting to enable the reliable deployment of open, scalable and expandable robotic systems. The conceptualization relies on employing multiple AI-empowered cognitive processes that operate at the edge nodes of a network of robotic platforms. The applicability of the proposed framework is explained using an example of a real-world industrial case study.
  arXiv  Detail & Related papers  (2020-12-15T09:49:22Z)
• Multi-Center Federated Learning [62.57229809407692]
  This paper proposes a novel multi-center aggregation mechanism for federated learning. It learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers. Our experimental results on benchmark datasets show that our method outperforms several popular federated learning methods.
  arXiv  Detail & Related papers  (2020-05-03T09:14:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.