PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers

[2512.03444] PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2512.03444 (cs) [Submitted on 3 Dec 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers Authors: Davood Soleymanzadeh , Xiao Liang , Minghui Zheng View a PDF of the paper titled PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers, by Davood Soleymanzadeh and 2 other authors View PDF HTML (experimental) Abstract: Deep learning methods have significantly enhanced motion planning for robotic manipulators by leveraging prior experiences within planning datasets. However, state-of-the-art neural motion planners are primarily trained on small datasets collected in manually generated workspaces, limiting their deployment in various everyday scenarios. Additionally, these planners often rely on monolithic network architectures that struggle to encode critical planning information. To address these challenges, we introduce Motion Policy with Dataset Synthesis powered by large language models (LLMs) and Fusion Action-Chunking Transformers (PerFACT), which incorporates two key components. Firstly, a novel workspace generation method, PerFACT, enables large-scale planning data collection by leveraging procedural primitive generation, and LLM-powered primitive suggestion and placement. Secondly, we introduce Fusion Motion Policy Networks (M$\pi$NetsFusion), an end-to-end, open-loop neural motion planner that uses a fusion action-chunking transformer to better encode planning signals and attend to multiple feature modalities. Leveraging PerFACT, we collect a dataset of 3.5M trajectories to train and evaluate M$\pi$NetsFusion against state-of-the-art planners. Results show that M$\pi$NetsFusion achieves consistently low planning time with sub-second inference, while maintaining competitive performance compared to both sampling-based and end-to-end neural benchmark planners. Project website: \href{ this https URL }{ this https URL } Subjects: Robotics (cs.RO) ; Systems and Control (eess.SY) Cite as: arXiv:2512.03444 [cs.RO] (or arXiv:2512.03444v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2512.03444 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Minghui Zheng [ view email ] [v1] Wed, 3 Dec 2025 04:54:55 UTC (5,735 KB) [v2] Sat, 15 Aug 2026 03:12:06 UTC (5,638 KB) Full-text links: Access Paper: View a PDF of the paper titled PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers, by Davood Soleymanzadeh and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2025-12 Change to browse by: cs cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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