MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping

• URL: http://arxiv.org/abs/2507.10158v2
• Date: Wed, 16 Jul 2025 12:39:22 GMT
• Title: MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping
• Authors: Obaidullah Zaland, Erik Elmroth, Monowar Bhuyan,
• Abstract summary: Federated Learning (FL) is a machine learning paradigm that enables participating devices to train privacy-preserved and collaborative models.<n>We propose MTF-Grasp, a multi-tier FL approach for robotic grasping, acknowledging the unique challenges posed by the non-IID data distribution across robots.<n>Our approach outperforms the conventional FL setup by up to 8% on the quantity-skewed Cornell and Jacquard grasping datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a promising machine learning paradigm that enables participating devices to train privacy-preserved and collaborative models. FL has proven its benefits for robotic manipulation tasks. However, grasping tasks lack exploration in such settings where robots train a global model without moving data and ensuring data privacy. The main challenge is that each robot learns from data that is nonindependent and identically distributed (non-IID) and of low quantity. This exhibits performance degradation, particularly in robotic grasping. Thus, in this work, we propose MTF-Grasp, a multi-tier FL approach for robotic grasping, acknowledging the unique challenges posed by the non-IID data distribution across robots, including quantitative skewness. MTF-Grasp harnesses data quality and quantity across robots to select a set of "top-level" robots with better data distribution and higher sample count. It then utilizes top-level robots to train initial seed models and distribute them to the remaining "low-level" robots, reducing the risk of model performance degradation in low-level robots. Our approach outperforms the conventional FL setup by up to 8% on the quantity-skewed Cornell and Jacquard grasping datasets.

Related papers

• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots [133.23509142762356]
  General-purpose robots need a versatile body and an intelligent mind.<n>Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy.<n>We introduce GR00T N1, an open foundation model for humanoid robots.
  arXiv  Detail & Related papers  (2025-03-18T21:06:21Z)
• Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation [7.8839937556789375]
  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.
  arXiv  Detail & Related papers  (2024-11-06T18:44:09Z)
• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learning [25.359270812682155]
  We investigate how to weigh different subsets or domains'' of robotics datasets for robot foundation model pre-training. Our method, Re-Mix, addresses the wide range of challenges that arise when applying DRO to robotics datasets.
  arXiv  Detail & Related papers  (2024-08-26T06:14:25Z)
• 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)
• Grasping Student: semi-supervised learning for robotic manipulation [0.7282230325785884]
  We design a semi-supervised grasping system that takes advantage of images of products to be picked, which are collected without any interactions with the robot. In the regime of a small number of robot training samples, taking advantage of the unlabeled data allows us to achieve performance at the level of 10-fold bigger dataset size.
  arXiv  Detail & Related papers  (2023-03-08T09:03:11Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• 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)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)

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.