外观
论文arXiv 2609.143101 个月前
VLBiMan++：扩展视觉-语言锚定的一次性双臂操作泛化边界
原文链接：VLBiMan++: Expanding the Generalization Boundary of Vision-Language Anchored One-Shot Bimanual Manipulation（arXiv 2609.14310 · 发布日期：2026-09-13）
VLBiMan++: Expanding the Generalization Boundary of Vision-Language Anchored One-Shot Bimanual Manipulation
VLBiMan++从单次人类示范分解并自适应迁移技能，在真实双臂操作中验证跨任务、物体、场景、平台与部署条件的泛化。
核心要点
- 可泛化双臂操作需要可复用任务先验，以减少大规模遥操作示范和策略重训成本。
- 方法从单次人类示范进行任务感知分解，并用视觉语言几何适应无重训迁移技能。
- 框架扩展到任务、物体、场景、异构双臂平台和长期闭环部署等五类泛化维度。
- 真实实验显示其在更具挑战设置下保持较强任务成功与适应能力。
验证范围与局限：验证范围为真实世界双臂操作实验，涵盖任务、物体、场景、异构平台和长期闭环部署条件。
英文摘要 (Abstract)
Generalizable bimanual robotic manipulation requires a reusable task prior that can persist across increasingly diverse tasks, objects, scenes, embodiments, and execution conditions, thus avoiding the prohibitive cost of large-scale teleoperated demonstrations and policy retraining. In this work, we present VLBiMan++, an extended framework that expands the generalization boundary of vision-language anchored one-shot bimanual manipulation. Starting from a single human demonstration, VLBiMan++ performs task-aware decomposition to identify reusable and adaptable skill components, and employs vision-language grounded geometric adaptation to transfer these skills to novel configurations without retraining. Building on this foundation, we systematically extend generalization along five dimensions: task generalization through diverse and long-horizon skill compositions; object generalization across unseen categories, varying geometries, and more complex articulated or deformable objects; scene generalization under clutter, occlusion, and dynamic interference; embodiment generalization across heterogeneous dual-arm robotic platforms; and deployment generalization through prolonged closed-loop execution under repeated external perturbations. To support this broader scope, we further introduce object-state-aware adaptation and lightweight trajectory optimization mechanisms that accommodate changes beyond simple rigid 6-DoF pose variations while preserving reliable bimanual coordination. Extensive real-world experiments demonstrate that VLBiMan++ maintains strong task success and adaptation capability across these increasingly challenging settings. Overall, VLBiMan++ advances one-shot bimanual manipulation from demonstrating isolated transferability toward a more systematic and scalable framework for generalization across tasks, objects, scenes, embodiments, and long-term deployment conditions.
作者：Huayi Zhou, Wei Gao, Yiyang Han, Kui Jia, Hui Huang
来源
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