DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation

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 baselines. We, humans, spend little or no effort perceiving and interacting with diverse 3D objects to accomplish everyday tasks in our daily lives. It is, however, an extremely challenging task for developing artificial intelligent robots to achieve similar capabilities due to the exceptionally rich 3D object space and high complexity manipulating with diverse 3D geometry for different downstream tasks. One of the core challenges in bridging the gaps is figuring out good visual representations of 3D objects that are generalizable across diverse 3D shapes at a large scale and directly consumable by downstream planners and controllers for robotic manipulation.