{"paper":{"title":"Adaptive Modular Geometric Control of Robotic Manipulators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Decomposing manipulator dynamics into modules enables local geometric controllers with a single adaptation gain that cut RMS position error by at least 12.2 percent.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Amir Hossein Barjini, Gokhan Alcan, Jouni Mattila, Mahdi Hejrati","submitted_at":"2026-03-04T11:55:53Z","abstract_excerpt":"This paper develops an adaptive modular geometric control framework for robotic manipulators with uncertain inertial parameters. The manipulator is decomposed into rigid-body and joint modules, where each rigid-body module is represented by an Euler-Poincar\\'e-type spatial dynamics on the Lie algebra se(3), and configuration errors are defined intrinsically through logarithmic maps on SE(3). The joint modules impose local screw constraints that relate adjacent body twists, accelerations, and transmitted wrenches, yielding a recursive propagation structure for the interconnected multibody syste"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The proposed adaptive modular geometric control framework reduces the RMS position error by at least 12.2% compared with state-of-the-art controllers under almost the same control effort, while the nominal case establishes exponential stability.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The overall manipulator dynamics can be decomposed into individual modules such that local geometric control laws can be designed independently while still guaranteeing global stability and performance.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"An adaptive modular geometric controller for robotic manipulators achieves exponential stability in nominal cases and reduces RMS position error by at least 12.2% versus state-of-the-art methods in simulations while using similar effort.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Decomposing manipulator dynamics into modules enables local geometric controllers with a single adaptation gain that cut RMS position error by at least 12.2 percent.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ded98f78dfcfcd6fd4ad181c0cccf1445accec8df0c724f5c29134a7d34e1f0d"},"source":{"id":"2603.03965","kind":"arxiv","version":3},"verdict":{"id":"ac2af56d-e449-4559-ae95-8793ea6aa6de","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T16:54:38.024348Z","strongest_claim":"The proposed adaptive modular geometric control framework reduces the RMS position error by at least 12.2% compared with state-of-the-art controllers under almost the same control effort, while the nominal case establishes exponential stability.","one_line_summary":"An adaptive modular geometric controller for robotic manipulators achieves exponential stability in nominal cases and reduces RMS position error by at least 12.2% versus state-of-the-art methods in simulations while using similar effort.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The overall manipulator dynamics can be decomposed into individual modules such that local geometric control laws can be designed independently while still guaranteeing global stability and performance.","pith_extraction_headline":"Decomposing manipulator dynamics into modules enables local geometric controllers with a single adaptation gain that cut RMS position error by at least 12.2 percent."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.03965/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}