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

• URL: http://arxiv.org/abs/2509.24956v1
• Date: Mon, 29 Sep 2025 15:50:51 GMT
• Title: MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation
• Authors: Jan Ole von Hartz, Lukas Schweizer, Joschka Boedecker, Abhinav Valada,
• Abstract summary: Multi-Stream Generative Policy (MSG) trains multiple object-centric policies and combines them at inference to improve generalization and sample efficiency.<n>MSG is model-agnostic and inference-only, hence widely applicable to various generative policies and training paradigms.
• Score: 17.246361335918866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 deployment. Finally, MSG enables zero-shot object instance transfer. We make our code publicly available at https://msg.cs.uni-freiburg.de.

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