{"paper":{"title":"Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A GRU dynamics model optimized end-to-end lets tendon-driven continuum robots track accurately and reject unseen payloads without self-excited oscillations.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fei Wang, Haojian Lu, Ke Qiu, Rong Xiong, Yue Wang, Ziqing Zou","submitted_at":"2026-04-28T14:20:44Z","abstract_excerpt":"Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control. We develop a GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction to effectively suppress compounding errors during long-horizon auto-regressive prediction. By treating this model as a gradient bridge, an end-to-end neural control policy is op"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction effectively suppresses compounding errors during long-horizon auto-regressive prediction, allowing the end-to-end neural policy to implicitly internalize compensation for intricate nonlinearities.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A bidirectional multi-channel GRU dynamics model with residual prediction supports end-to-end neural control for tendon-driven continuum robots, delivering accurate tracking and robustness to unseen payloads without self-excited oscillations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A GRU dynamics model optimized end-to-end lets tendon-driven continuum robots track accurately and reject unseen payloads without self-excited oscillations.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ed8840b231beeb0e5096282b7cdb1a1665134682aa14afaed761cf7f8ef307d3"},"source":{"id":"2604.25691","kind":"arxiv","version":2},"verdict":{"id":"767b50a8-119c-4c1f-8190-7ce0b0f4a612","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T15:50:44.381342Z","strongest_claim":"Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations.","one_line_summary":"A bidirectional multi-channel GRU dynamics model with residual prediction supports end-to-end neural control for tendon-driven continuum robots, delivering accurate tracking and robustness to unseen payloads without self-excited oscillations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction effectively suppresses compounding errors during long-horizon auto-regressive prediction, allowing the end-to-end neural policy to implicitly internalize compensation for intricate nonlinearities.","pith_extraction_headline":"A GRU dynamics model optimized end-to-end lets tendon-driven continuum robots track accurately and reject unseen payloads without self-excited oscillations."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.25691/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T04:35:26.571767Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:52:14.461536Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"7e357e3ffe8597852081b915e325b7770fae13dc1bb274cdcc529bd3b4957be6"},"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"}