{"ID":6023570,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T13:03:38.548899896Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06256","arxiv_id":"2607.06256","title":"Diagnosing Semantic Handoff Failures in Agent-Orchestrated Vision-Language-Action Skill Composition","abstract":"Long-horizon household tasks require robots to compose many language-conditioned skills, yet the boundary between consecutive skills is rarely explicit. A skill may satisfy its own postcondition while leaving the robot, objects, or camera views in a state from which the next skill cannot reliably start. We study this semantic handoff problem in BEHAVIOR-1K through an agent-orchestrated vision-language-action execution harness. The harness invokes $π_{0.5}$-based skill checkpoints trained from cleaned BEHAVIOR-1K demonstrations, assigns each skill typed arguments and a step budget, and uses multi-view vision-language model verification to decide whether execution should advance, retry, or replan. To separate isolated skill competence from long-horizon compositional robustness, we evaluate the same checkpoints under two initial-state distributions: clean skill-boundary snapshots and chained terminal states produced by previous skills. Selected navigation, grasping, placement, and door-opening skills achieve 77--100% success from clean snapshots under human-reviewed verification, yet composed rollouts still frequently stall from chained states. Execution traces attribute these failures to next-skill readiness, target grounding, and low-level control execution, revealing a substantial gap between single-skill success and reliable long-horizon task completion. These findings turn near-zero end-to-end task success into actionable diagnostics, showing that future VLA skill libraries must learn robustness to the messy chained-state distribution that clean demonstrations systematically underrepresent.","short_abstract":"Long-horizon household tasks require robots to compose many language-conditioned skills, yet the boundary between consecutive skills is rarely explicit. A skill may satisfy its own postcondition while leaving the robot, objects, or camera views in a state from which the next skill cannot reliably start. We study this s...","url_abs":"https://arxiv.org/abs/2607.06256","url_pdf":"https://arxiv.org/pdf/2607.06256v1","authors":"[\"Ke Rui\",\"Yushen Zuo\",\"Jiawei Wang\",\"Haoran Jia\",\"Jinming Ma\",\"Weitao Zhou\",\"Minglei Li\"]","published":"2026-07-07T13:24:37Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Language Model\"]","has_code":false}