{"ID":2826271,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19390","arxiv_id":"2512.19390","title":"TwinAligner: Visual-Dynamic Alignment Empowers Physics-aware Real2Sim2Real for Robotic Manipulation","abstract":"The robotics field is evolving towards data-driven, end-to-end learning, inspired by multimodal large models. However, reliance on expensive real-world data limits progress. Simulators offer cost-effective alternatives, but the gap between simulation and reality challenges effective policy transfer. This paper introduces TwinAligner, a novel Real2Sim2Real system that addresses both visual and dynamic gaps. The visual alignment module achieves pixel-level alignment through SDF reconstruction and editable 3DGS rendering, while the dynamic alignment module ensures dynamic consistency by identifying rigid physics from robot-object interaction. TwinAligner improves robot learning by providing scalable data collection and establishing a trustworthy iterative cycle, accelerating algorithm development. Quantitative evaluations highlight TwinAligner's strong capabilities in visual and dynamic real-to-sim alignment. This system enables policies trained in simulation to achieve strong zero-shot generalization to the real world. The high consistency between real-world and simulated policy performance underscores TwinAligner's potential to advance scalable robot learning. Code and data will be released on https://twin-aligner.github.io","short_abstract":"The robotics field is evolving towards data-driven, end-to-end learning, inspired by multimodal large models. However, reliance on expensive real-world data limits progress. Simulators offer cost-effective alternatives, but the gap between simulation and reality challenges effective policy transfer. This paper introduc...","url_abs":"https://arxiv.org/abs/2512.19390","url_pdf":"https://arxiv.org/pdf/2512.19390v1","authors":"[\"Hongwei Fan\",\"Hang Dai\",\"Jiyao Zhang\",\"Jinzhou Li\",\"Qiyang Yan\",\"Yujie Zhao\",\"Mingju Gao\",\"Jinghang Wu\",\"Hao Tang\",\"Hao Dong\"]","published":"2025-12-22T13:38:11Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"cs.GR\"]","methods":"[]","has_code":false}