Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulation datasets. Simulation and synthetic data generation offer a scalable alternative, but their usefulness is limited by the visual domain gap between simulation and reality. In this work, we present Point Bridge, a framework that leverages unified, domain-agnostic point-based representations to unlock synthetic datasets for zero-shot sim-to-real policy transfer, without explicit visual or object-level alignment. Point Bridge combines automated point-based representation extraction via Vision-Language Models (VLMs), transformer-based policy learning, and efficient inference-time pipelines to train capable real-world manipulation agents using only synthetic data. With additional co-training on small sets of real demonstrations, Point Bridge further improves performance, substantially outperforming prior vision-based sim-and-real co-training methods. It achieves up to 44% gains in zero-shot sim-to-real transfer and up to 66% with limited real data across both single-task and multitask settings. Videos of the robot are best viewed at: https://pointbridge3d.github.io/

翻译：机器人基础模型正开始兑现通用机器人智能体的承诺，但其进展仍受限于大规模真实世界操作数据集的稀缺性。仿真与合成数据生成提供了可扩展的替代方案，然而仿真与现实之间的视觉领域差距限制了其实用性。本文提出Point Bridge框架，该框架利用统一的、与领域无关的基于点的表示，无需显式的视觉或对象级对齐即可解锁合成数据集用于零样本仿真到现实策略迁移。Point Bridge通过视觉语言模型（VLM）自动提取基于点的表示、基于Transformer的策略学习以及高效推理流水线相结合，仅使用合成数据即可训练出具备真实世界操作能力的智能体。在少量真实示范动作的额外协同训练下，Point Bridge进一步提升了性能，显著优于以往的基于视觉的仿真与真实协同训练方法。在单任务与多任务场景中，该框架在零样本仿真到现实迁移中实现了高达44%的性能提升，在有限真实数据条件下则实现了66%的提升。机器人操作视频请参见：https://pointbridge3d.github.io/