{"paper":{"title":"Integrated Hierarchical Decision-Making in Inverse Kinematic Planning and Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A sparse hierarchical nonlinear programming solver integrates decision-making with inverse kinematic planning and control via the ℓ0-norm.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Aberrahmane Kheddar, Gordon Boateng, Kai Pfeiffer, Quan Zhang, Vincent Bonnet, Yuqing Chen, Yuquan Wang","submitted_at":"2024-12-02T09:45:32Z","abstract_excerpt":"This work presents a novel and efficient nonlinear programming framework that tightly integrates hierarchical decision-making with whole-body inverse kinematic planning and control. Decision-making plays a central role in many aspects of robotics, from sparse inverse kinematic control with a minimal number of joints, to inverse kinematic planning while simultaneously selecting a discrete end-effector location from multiple candidates. Current approaches often rely on heavy computations using mixed-integer nonlinear programming, separate decision-making from inverse kinematics (some times appro"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The proposed sparse hierarchical nonlinear programming solver is efficient, versatile, and accurate by exploiting sparse hierarchical structure and leveraging the ℓ0-norm which is rarely used in robotics. The solver efficiently tackles complex nonlinear hierarchical decision-making problems previously unaddressed in the literature, such as inverse kinematic planning with simultaneous prioritized selection of end-effector locations from a large set of candidates, or inverse kinematic control with simultaneous selection of bi-manual grasp locations on a randomly rotated box.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that leveraging the ℓ0-norm in a nonlinear programming formulation will yield an efficient and accurate solver for these integrated hierarchical problems without introducing intractable non-convexity or requiring approximations that undermine the claimed advantages over mixed-integer methods.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A sparse hierarchical nonlinear programming solver integrates decision-making with inverse kinematic planning and control in robotics using l0-norm sparsity.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A sparse hierarchical nonlinear programming solver integrates decision-making with inverse kinematic planning and control via the ℓ0-norm.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"22e278e8eaacf40c65e91bda8eedca4a51bcfd30d884f73b6232797a4765751d"},"source":{"id":"2412.01324","kind":"arxiv","version":5},"verdict":{"id":"01be88f2-2730-47b3-b154-8a102c0f46fa","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-23T07:59:43.975370Z","strongest_claim":"The proposed sparse hierarchical nonlinear programming solver is efficient, versatile, and accurate by exploiting sparse hierarchical structure and leveraging the ℓ0-norm which is rarely used in robotics. The solver efficiently tackles complex nonlinear hierarchical decision-making problems previously unaddressed in the literature, such as inverse kinematic planning with simultaneous prioritized selection of end-effector locations from a large set of candidates, or inverse kinematic control with simultaneous selection of bi-manual grasp locations on a randomly rotated box.","one_line_summary":"A sparse hierarchical nonlinear programming solver integrates decision-making with inverse kinematic planning and control in robotics using l0-norm sparsity.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that leveraging the ℓ0-norm in a nonlinear programming formulation will yield an efficient and accurate solver for these integrated hierarchical problems without introducing intractable non-convexity or requiring approximations that undermine the claimed advantages over mixed-integer methods.","pith_extraction_headline":"A sparse hierarchical nonlinear programming solver integrates decision-making with inverse kinematic planning and control via the ℓ0-norm."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.01324/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"}