ProcVLM：基于学习的程序导向进度奖励用于机器人操作
ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation
AI总结：
ProcVLM通过程序结构和阶段内视觉变化学习密集的进度奖励，提升机器人操作的程序推理能力，优于现有基线方法。
摘要：
长周期的机器人操作需要反映任务通过其程序阶段进展的密集反馈，而非仅是否最终成功。现有奖励模型常依赖轨迹级成功标签或时间插值，可能混淆时间流逝与真实任务进展，无法捕捉未完成步骤、停滞和失败状态。我们提出ProcVLM，一种程序感知的视觉-语言模型，学习程序导向的进度作为操作的密集奖励信号。而非从终端结果或时间代理推导进度，ProcVLM将进度估计基于程序结构和阶段内视觉变化，并进一步采用推理先于估计的范式，在估计任务进展前推断剩余基本动作。具体而言，我们通过合成帧级子任务语义注释构建监督，根据子任务结构分配进度预算，并基于子任务内视觉变化分配每个预算。为大规模训练ProcVLM，我们构建了标准化的程序监督合成管道，从30个具身数据集中构建了60M标注帧的ProcCorpus-60M，从中推导出ProcVQA用于程序感知预训练，以进度估计为核心任务，同时包含动作分割和未来规划。在ProcVQA和奖励模型基准测试中，ProcVLM在具身程序推理方面表现更优，产生更具判别性的轨迹内部进度估计，支持其作为下游奖励引导策略优化的密集奖励模型。项目页面：https://procvlm.github.io/
英文摘要：
Long-horizon robotic manipulation requires dense feedback that reflects how a task advances through its procedural stages, not merely whether the final outcome is successful. Existing reward models often rely on trajectory-level success labels or time-based interpolation, which can conflate elapsed time with true task progress and therefore fail to capture unfinished steps, stagnation, and failure states. We present ProcVLM, a progress-aware vision-language model that learns procedure-grounded progress as a dense reward signal for manipulation. Rather than deriving progress from terminal outcomes or temporal proxies, ProcVLM grounds progress estimation in procedural structure and intra-stage visual change, and further adopts a reasoning-before-estimation paradigm that infers the remaining atomic actions before estimating task progress. Specifically, we construct this supervision by synthesizing frame-level subtask-semantic annotations, assigning progress budgets according to subtask structure, and distributing each budget based on intra-subtask visual change. To train ProcVLM at scale, we build a standardized procedural supervision synthesis pipeline and construct ProcCorpus-60M from 30 embodied datasets with 60M annotated frames, from which we derive ProcVQA for procedure-aware pretraining, with progress estimation as the central task alongside action segmentation and future planning. Experiments on ProcVQA and reward-model benchmarks show that ProcVLM improves embodied procedural reasoning and yields more discriminative trajectory-internal progress estimates than representative baselines, supporting its use as a dense reward model for downstream reward-guided policy optimization. Project page: https://procvlm.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
