Robotic manipulation of ungraspable objects with two-finger grippers presents
significant challenges due to the paucity of graspable features, while
traditional pre-grasping techniques, which rely on repositioning objects and
leveraging external aids like table edges, lack the adaptability across object
categories and scenes. Addressing this, we introduce PreAfford, a novel
pre-grasping planning framework that utilizes a point-level affordance
representation and a relay training approach to enhance adaptability across a
broad range of environments and object types, including those previously
unseen. Demonstrated on the ShapeNet-v2 dataset, PreAfford significantly
improves grasping success rates by 69% and validates its practicality through
real-world experiments. This work offers a robust and adaptable solution for
manipulating ungraspable objects.

Robotic manipulation of ungraspable objects is improved using the PreAfford pre-grasping planning framework, leading to a significant increase in grasping success rates.

PreAfford: 基于普适性可操作性的多样物体和环境预抓取

PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects  and Environments

The progressive prevalence of robots in human-suited environments has given
rise to a myriad of object manipulation techniques, in which dexterity plays a
paramount role. It is well-established that humans exhibit extraordinary
dexterity when handling objects. Such dexterity seems to derive from a robust
understanding of object properties (such as weight, size, and shape), as well
as a remarkable capacity to interact with them. Hand postures commonly
demonstrate the influence of specific regions on objects that need to be
grasped, especially when objects are partially visible. In this work, we
leverage human-like object understanding by reconstructing and completing their
full geometry from partial observations, and manipulating them using a 7-DoF
anthropomorphic robot hand. Our approach has significantly improved the
grasping success rates of baselines with only partial reconstruction by nearly
30% and achieved over 150 successful grasps with three different object
categories. This demonstrates our approach's consistent ability to predict and
execute grasping postures based on the completed object shapes from various
directions and positions in real-world scenarios. Our work opens up new
possibilities for enhancing robotic applications that require precise grasping
and manipulation skills of real-world reconstructed objects.

通过从部分观测中重建和完成其全面几何结构，并利用七自由度的拟人化机器人手进行操作，我们的方法显著提高了只有部分重建的基线的抓取成功率近 30％，并以三个不同的对象类别多次实现了超过 150 次成功抓取，这表明我们的方法在真实世界的不同方向和位置基于完成的对象形状进行预测和执行抓取姿势的一贯能力，为增强需要实际重建对象的精确抓取和操作技能的机器人应用开辟了新的可能性。