让机器人理解短期意图，避免动作冲突，提升操作稳定性。
IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
- 用历史视觉信息编码短期意图，指导动作生成
- 在12个任务上显著提升执行稳定性与成功率
- 适合需要精细动作规划的机器人场景
机器人模仿数据常具多模态性：相似的视觉-语言观测可能对应不同动作序列，因示范者具有不同短期意图、任务阶段或近期上下文。现有帧条件型视觉-语言代理（VLA）策略仅依赖当前观测和指令推理每段动作，部分可观测下会在相邻重规划步骤中反复采样不同意图，导致动作段间冲突和执行不稳定。本文提出IntentVLA，一种历史条件型VLA框架，将近期视觉观测编码为紧凑的短期意图表征，并用于条件化动作段生成。我们还构建了AliasBench，一个基于RoboTwin2的12任务歧义感知基准，包含匹配的训练数据与评估环境，专门隔离短期观测混淆问题。在AliasBench、SimplerEnv、LIBERO和RoboCasa多个数据集上，IntentVLA均提升轨迹稳定性，超越强基线模型。
原文摘要 · Abstract (English)
Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context. Existing frame-conditioned VLA policies infer each chunk from the current observation and instruction alone, so under partial observability they may resample different intents across adjacent replanning steps, leading to inter-chunk conflict and unstable execution. We introduce IntentVLA, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation. We further introduce AliasBench, a 12-task ambiguity-aware benchmark on RoboTwin2 with matched training data and evaluation environments that isolate short-horizon observation aliasing. Across AliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines
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