Object affordance and volumetric information are essential in devising effective grasping strategies under task-specific constraints. This paper presents an approach for inferring suitable grasping strategies from limited partial views of an object. To achieve this, a recurrent generative adversarial network (R-GAN) was proposed by incorporating a recurrent generator with long short-term memory (LSTM) units for it to process a variable number of depth scans. To determine object affordances, the AffordPose knowledge dataset is utilized as prior knowledge. Affordance retrieving is defined by the volume similarity measured via Chamfer Distance and action similarities. A Proximal Policy Optimization (PPO) reinforcement learning model is further implemented to refine the retrieved grasp strategies for task-oriented grasping. The retrieved grasp strategies were evaluated on a dual-arm mobile manipulation robot with an overall grasping accuracy of 89% for four tasks: lift, handle grasp, wrap grasp, and press.

翻译：物体可供性与体积信息对于在任务特定约束下制定有效的抓取策略至关重要。本文提出了一种从物体的有限部分视角推断合适抓取策略的方法。为实现此目标，我们提出了一种循环生成对抗网络（R-GAN），其通过引入带有长短期记忆（LSTM）单元的循环生成器来处理可变数量的深度扫描。为确定物体可供性，我们利用AffordPose知识数据集作为先验知识。可供性检索通过以倒角距离测量的体积相似度和动作相似度来定义。进一步实现了近端策略优化（PPO）强化学习模型，以优化检索到的抓取策略，使其适用于面向任务的抓取。检索到的抓取策略在双臂移动操作机器人上进行了评估，在提升、手柄抓取、包裹抓取和按压四项任务中，总体抓取准确率达到89%。