[2509.24956] MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation

Computer Science > Robotics arXiv:2509.24956 (cs) [Submitted on 29 Sep 2025 (v1), last revised 31 Mar 2026 (this version, v2)] Title:MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation Authors:Jan Ole von Hartz, Lukas Schweizer, Joschka Boedecker, Abhinav Valada View a PDF of the paper titled MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation, by Jan Ole von Hartz and 3 other authors View PDF HTML (experimental) Abstract:Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency, it does not resolve this limitation. In this work, we propose Multi-Stream Generative Policy (MSG), an inference-time composition framework that trains multiple object-centric policies and combines them at inference to improve generalization and sample efficiency. MSG is model-agnostic and inference-only, hence widely applicable to various generative policies and training paradigms. We perform extensive experiments both in simulation and on a real robot, demonstrating that our approach learns high-quality generative policies from as few as five demonstrations, resulting in a 95% reduction in demonstrations, and improves policy performance by 89 percent compared to single-stream approaches. Furthermore, we present comprehensive ablation studies on various composition strategies and provide practical recommendations for deplo...