AFFORD2ACT：基于 affordance 的自动关键点选择用于通用且轻量的机器人操作
AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation
AI总结：
本文提出 AFFORD2ACT 框架，通过文本提示和单张图像生成最小化语义 2D 关键点，实现轻量级机器人操作，提升数据效率和泛化能力。
摘要：
基于视觉的机器人学习常依赖密集图像或点云输入，计算成本高且易混入无关背景特征。现有关键点方法虽能聚焦操作相关特征但受限于手动启发式或任务耦合选择。本文提出 AFFORD2ACT 框架，通过 affordance 过滤、类别级关键点构建和基于 transformer 的策略学习，生成 38 维紧凑状态策略，可在 15 分钟内训练完成，实现实时操作无需本体感觉或密集表示。在多样化的现实任务中，AFFORD2ACT 显著提升数据效率，在未见过的物体、新类别、背景和干扰源上达到 82% 的成功率。
英文摘要：
Vision-based robot learning often relies on dense image or point-cloud inputs, which are computationally heavy and entangle irrelevant background features. Existing keypoint-based approaches can focus on manipulation-centric features and be lightweight, but either depend on manual heuristics or task-coupled selection, limiting scalability and semantic understanding. To address this, we propose AFFORD2ACT, an affordance-guided framework that distills a minimal set of semantic 2D keypoints from a text prompt and a single image. AFFORD2ACT follows a three-stage pipeline: affordance filtering, category-level keypoint construction, and transformer-based policy learning with embedded gating to reason about the most relevant keypoints, yielding a compact 38-dimensional state policy that can be trained in 15 minutes, which performs well in real-time without proprioception or dense representations. Across diverse real-world manipulation tasks, AFFORD2ACT consistently improves data efficiency, achieving an 82% success rate on unseen objects, novel categories, backgrounds, and distractors.
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。
