[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85835-en":3,"doc-seo-85835-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85835,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","On the Efficiency of LoRA Fine-Tuning for Vision-Language-Action Models in Industrial Robotic Manipulation","Deploying billion-parameter Vision-Language-Action (VLA) models on industrial robots demands fine-tuning to close the embodiment gap between pretraining data and target manipulator kinematics. A systematic evaluation studies Low-Rank Adaptation (LoRA) for a flow-matching VLA, π0, on four UR5e precision assembly tasks. Experiments sweep LoRA ranks, compare adapter allocation, and test freezing and vision-encoder adaptation variants. Results show performance saturates at r=32, and LoRA can match full fine-tuning while reducing peak static VRAM from 36.2 to 10.8 GiB.","arXiv :2607 . 10172v1 [ cs .RO] 11 Jul 2026  \nOn the Efficiency of LoRA Fine-Tuning for Vision-Language-Action Models in Industrial Robotic Manipulation  \nFinn Ferchau 1 ,2 , Daniel Pommer 1[0009−0002−4109−4763], and  \nCristian Axenie 1 ,3[0000−0001−6184−0546]  \n1 Technische Hochschule Nürnberg Georg Simon Ohm, Nürnberg, Germany  \ncristian.axenie, [daniel.pommer@th-nuernberg.de](daniel.pommer@th-nuernberg.de)  \n2 Siemens AG, München, Germany  \n[finn.ferchau@siemens.com](finn.ferchau@siemens.com)  \n3 Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany  \n[cristian.axenie@iis.fraunhofer.de](cristian.axenie@iis.fraunhofer.de)  \nAbstract. Deploying billion-parameter Vision-Language-Action (VLA) models on industrial hardware requires fine-tuning to bridge the embodiment gap. Full Fine-Tuning (FFT) provides maximal plasticity but requires data centre-grade GPUs. We present a systematic study of LowRank Adaptation (LoRA) for π0 , a flow-matching VLA, evaluated on four precision assembly tasks with a UR5e robotic manipulator. Across a sweep of LoRA ranks (r=8 to 256), allocation strategies, and componentfreezing ablations, we find no statistically significant advantage of FFT over certain LoRA configurations. Performance saturates at r=32, and uniform allocation across the Vision-Language-Model (VLM) backbone and action expert proves sufficient. Freezing the VLM or restricting the vision encoder to LoRA significantly degrades performance, indicating that embodiment adaptation requires both semantic and visual plasticity. These results suggest that LoRA at r=32 with full vision encoder fine-tuning is a practical approach, reducing static peak VRAM from  \n36.2 to 10.8 GiB (parameters and optimizer states, activation memory excluded) without detectable performance loss.  \nKeywords: Parameter-Efficient Fine-Tuning · VLA Models · Industrial Manipulation  \n1 Introduction  \nIndustrial assembly has traditionally relied on precisely programmed robotic systems that excel in repetitive, high-volume production but require costly reprogramming when tasks or parts change [10] . Vision-Language-Action (VLA) models promise to address this rigidity by enabling robots to execute manipulation tasks from natural-language instructions, thereby replacing explicit programming with learning from demonstrations [3,2] . However, deploying these foundation models on specific industrial hardware is not straightforward: a critical embodiment gap exists between the heterogeneous pre-training data and  \n2 F. Ferchau, D. Pommer, and C. Axenie  \nthe kinematics of a target manipulator, necessitating fine-tuning for real-world deployment [2] .  \nFull Fine-Tuning (FFT) updates all model parameters and provides maximal plasticity, but for large models, it requires data centre-grade GPUs. This often forces practitioners to transfer proprietary training data to external cloud providers, potentially conflicting with industrial data privacy requirements [18] . Low-Rank Adaptation (LoRA) [9] offers a parameter-efficient alternative by injecting trainable low-rank matrices while keeping the pre-trained weights frozen, reducing GPU memory requirements.  \nWhile LoRA has shown promising results in natural language processing and vision tasks, its application to VLA models for robotic manipulation raises two questions that prior work has not addressed. First, how does robotic manipulation performance scale with LoRA rank—the factorization dimension that bounds the true matrix rank of the weight update? Existing evaluations test one or two configurations without characterizing the full capacity–performance curve. Second, and specific to flow-matching VLAs: where should adapter capacity be allocated? Flow-matching VLAs such as π0 [2] separate a VLM backbone from a dedicated action expert (AE) that generates continuous motor commands (Section 2.1) . Yet, no prior work has investigated how to distribute a fixed adapter budget between these two components.  \nWe address","cbCaigILqs1EbKK2","https://ap.wps.com/l/cbCaigILqs1EbKK2","pdf",19965680,5,1,12,"English","en",105,"# Introduction\n# Related Work\n## Vision-Language-Action Models","[{\"question\":\"What problem does the paper address for deploying VLA models in industrial robotics?\",\"answer\":\"It targets the embodiment gap between heterogeneous pretraining data and the kinematics of a specific manipulator, which requires fine-tuning for real-world deployment.\"},{\"question\":\"How does LoRA performance change as the LoRA rank increases?\",\"answer\":\"Across ranks from r=8 to 256, performance saturates at r=32, and full fine-tuning shows no statistically significant advantage over certain LoRA configurations.\"},{\"question\":\"Which adapter allocation strategy is sufficient for π0 in the studied tasks?\",\"answer\":\"Uniform allocation across the Vision-Language-Model (VLM) backbone and the action expert is sufficient, and asymmetric allocations are evaluated to determine where capacity should be spent.\"}]",1784206593,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"on-the-efficiency-of-lora-fine-tuning-for-vision-language-action-models-in-industrial-robotic-manipulation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/on-the-efficiency-of-lora-fine-tuning-for-vision-language-action-models-in-industrial-robotic-manipulation/85835/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address for deploying VLA models in industrial robotics?","Question",{"text":76,"@type":77},"It targets the embodiment gap between heterogeneous pretraining data and the kinematics of a specific manipulator, which requires fine-tuning for real-world deployment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does LoRA performance change as the LoRA rank increases?",{"text":81,"@type":77},"Across ranks from r=8 to 256, performance saturates at r=32, and full fine-tuning shows no statistically significant advantage over certain LoRA configurations.",{"name":83,"@type":74,"acceptedAnswer":84},"Which adapter allocation strategy is sufficient for π0 in the studied tasks?",{"text":85,"@type":77},"Uniform allocation across the Vision-Language-Model (VLM) backbone and the action expert is sufficient, and asymmetric allocations are evaluated to determine where capacity should be 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