title: FLAME: A Federated Learning Benchmark for Robotic Manipulation
summary: Recent progress in robotic manipulation has been fueled by large-scale
  datasets collected across diverse environments. Training robotic manipulation
  policies on these datasets is traditionally performed in a centralized manner,
  raising concerns regarding scalability, adaptability, and data privacy. While
  federated learning enables decentralized, privacy-preserving training, its
  application to robotic manipulation remains largely unexplored. We introduce
  FLAME (Federated Learning Across Manipulation Environments), the first
  benchmark designed for federated learning in robotic manipulation. FLAME
  consists of: (i) a set of large-scale datasets of over 160,000 expert
  demonstrations of multiple manipulation tasks, collected across a wide range of
  simulated environments; (ii) a training and evaluation framework for robotic
  policy learning in a federated setting. We evaluate standard federated learning
  algorithms in FLAME, showing their potential for distributed policy learning
  and highlighting key challenges. Our benchmark establishes a foundation for
  scalable, adaptive, and privacy-aware robotic learning.
paper_authors._id: 67c92e8b5650d7efeba5b48c
paper_authors.name: Santiago Bou Betran
paper_authors._id: 67c92e8b5650d7efeba5b48d
paper_authors.name: Alberta Longhini
paper_authors._id: 67c92e8b5650d7efeba5b48e
paper_authors.name: Miguel Vasco
paper_authors._id: 67c92e8b5650d7efeba5b48f
paper_authors.name: Yuchong Zhang
paper_authors._id: 67c92e8b5650d7efeba5b490
paper_authors.name: Danica Kragic
paper_title: FLAME: A Federated Learning Benchmark for Robotic Manipulation
paper_summary: Recent progress in robotic manipulation has been fueled by large-scale
  datasets collected across diverse environments. Training robotic manipulation
  policies on these datasets is traditionally performed in a centralized manner,
  raising concerns regarding scalability, adaptability, and data privacy. While
  federated learning enables decentralized, privacy-preserving training, its
  application to robotic manipulation remains largely unexplored. We introduce
  FLAME (Federated Learning Across Manipulation Environments), the first
  benchmark designed for federated learning in robotic manipulation. FLAME
  consists of: (i) a set of large-scale datasets of over 160,000 expert
  demonstrations of multiple manipulation tasks, collected across a wide range of
  simulated environments; (ii) a training and evaluation framework for robotic
  policy learning in a federated setting. We evaluate standard federated learning
  algorithms in FLAME, showing their potential for distributed policy learning
  and highlighting key challenges. Our benchmark establishes a foundation for
  scalable, adaptive, and privacy-aware robotic learning.
paper_ai_keywords: federated learning decentralized privacy-preserving benchmark federated learning algorithms distributed policy learning