外观
论文arXiv 2609.292041 个月前
AdaHVLA：面向长程视觉-语言-动作执行的自适应支架
原文链接：AdaHVLA: Adaptive Harnesses for Long-Horizon Vision-Language-Action Execution（arXiv 2609.29204 · 发布日期：2026-09-24）
AdaHVLA: Adaptive Harnesses for Long-Horizon Vision-Language-Action Execution
AdaHVLA通过机器人经验自适应修订代码化协调策略，在仿真与真机部署中提升长程VLA执行。
核心要点
- VLA模型具备局部控制和指令跟随能力，但在需持续记忆与规划的长程任务中常遇到困难。
- 该方法将证据分析、支架修订与行为评估解耦，并用可测试协调假设指导修订和后续执行。
- 有状态修订图关联执行证据、假设、修订和效果，保留备选支架与跨任务适应记忆。
- 仿真中NaVILA-LH平均测试成功率由22.5%升至最高57.5%，三种VLA骨干上操控成功率最多提升30.8个百分点。
验证范围与局限：验证包括仿真中的NaVILA-LH和三种VLA骨干的操控测试，并有真机部署展示跨任务阶段的稳定执行。
英文摘要 (Abstract)
Vision-language-action (VLA) models offer strong local control and instruction following but often struggle with long-horizon tasks requiring persistent memory and planning. Task harnesses provide persistent context for agent reasoning by retaining task history and tracking progress across execution stages. To bring these complementary capabilities together, we introduce AdaHVLA, an adaptive harness that refines code-based coordination policies through robot experience to better align agent reasoning and memory with VLA execution. Its decoupled multiagent adaptation process separates evidence analysis, harness revision, and behavioral assessment into distinct working contexts, using testable coordination hypotheses to guide revisions and subsequent rollouts to assess their predicted effects. A stateful revision graph links execution evidence, hypotheses, revisions, and observed effects, preserving alternative harnesses and adaptation memory to guide refinement across repeated attempts and continued adaptation across tasks and environments. In simulation, AdaHVLA raises mean test success on NaVILA-LH from 22.5% to as high as 57.5% and improves manipulation test success across three VLA backbones by up to 30.8 percentage points over the initial harness. Real-world deployment further illustrates how the adapted policies support stable execution across task stages.
作者：Junyi Tang, Jie Peng, Zezhen Ding, Yuan Shen, Tianlong Chen
来源
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