Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments

TL;DR

This paper introduces a module-wise federated learning framework for grasp pose detection that reduces communication costs while maintaining high accuracy, enabling effective decentralized training in resource-limited robotic systems.

Contribution

It proposes a novel two-phase, module-wise federated learning approach that allocates communication resources based on module convergence rates, improving efficiency for robotic grasp detection.

Findings

Outperforms FedAvg and baselines in accuracy at lower communication costs

Achieves higher grasp success rates in cluttered environments

Validates effectiveness through real-world robot experiments

Abstract

Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substantial communication overhead of large models, a key issue for resource-constrained robots. To address this, we propose a novel module-wise FL framework that begins by analyzing the learning dynamics of the GPD model's functional components. This analysis identifies slower-converging modules, to which our framework then allocates additional communication effort. This is realized through a two-phase process: a standard full-model training phase is followed by a communication-efficient phase where only the identified subset of slower-converging modules is trained and their partial updates are aggregated. Extensive experiments on the GraspNet-1B dataset demonstrate that our method outperforms standard FedAvg and other baselines, achieving higher accuracy for a given communication budget. Furthermore, real-world experiments on a physical robot validate our approach, showing a superior grasp success rate compared to baseline methods in cluttered scenes. Our work presents a communication-efficient framework for training robust, generalized GPD models in a decentralized manner, effectively improving the trade-off between communication cost and model performance.