  It is essential yet challenging for future home-assistant robots to
understand and manipulate diverse 3D objects in daily human environments.
Towards building scalable systems that can perform diverse manipulation tasks
over various 3D shapes, recent works have advocated and demonstrated promising
results learning visual actionable affordance, which labels every point over
the input 3D geometry with an action likelihood of accomplishing the downstream
task (e.g., pushing or picking-up). However, these works only studied
single-gripper manipulation tasks, yet many real-world tasks require two hands
to achieve collaboratively. In this work, we propose a novel learning
framework, DualAfford, to learn collaborative affordance for dual-gripper
manipulation tasks. The core design of the approach is to reduce the quadratic
problem for two grippers into two disentangled yet interconnected subtasks for
efficient learning. Using the large-scale PartNet-Mobility and ShapeNet
datasets, we set up four benchmark tasks for dual-gripper manipulation.
Experiments prove the effectiveness and superiority of our method over three
baselines.
