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Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e. g. picking, placing, pulling, pushing, navigating).
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer
Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous.
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