FLAME: A Federated Learning Benchmark for Robotic Manipulation
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.
67c92e8b5650d7efeba5b48c
Santiago Bou Betran
67c92e8b5650d7efeba5b48d
Alberta Longhini
67c92e8b5650d7efeba5b48e
Miguel Vasco
67c92e8b5650d7efeba5b48f
Yuchong Zhang
67c92e8b5650d7efeba5b490
Danica Kragic
FLAME: A Federated Learning Benchmark for Robotic Manipulation
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.
FLAME, a benchmark for federated learning in robotic manipulation, evaluates distributed policy learning using large-scale datasets across simulated environments.
federated learning robotic manipulation expert demonstrations manipulation tasks simulated environments training framework evaluation framework