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Nov 05, 2023
The world around us is full of soft objects that we as humans learn to perceive and deform with dexterous hand movements from a young age. In order for a Robotic hand to be able to control soft objects, it needs to acquire online state feedback of the deforming object. While RGB-D cameras can collect occluded information at a rate of 30 Hz, the latter does not represent a continuously trackable object surface. Hence, in this work, we developed a method that can create deforming meshes of deforming point clouds at a speed of above 50 Hz for different categories of objects. The reconstruction of meshes from point clouds has been long studied in the field of Computer graphics under 3D reconstruction and 4D reconstruction, however both lack the speed and generalizability needed for robotics applications. Our model is designed using a point cloud auto-encoder and a Real-NVP architecture. The latter is a continuous flow neural network with manifold-preservation properties. Our model takes a template mesh which is the mesh of an object in its canonical state and then deforms the template mesh to match a deformed point cloud of the object. Our method can perform mesh reconstruction and tracking at a rate of 58 Hz for deformations of six different ycb categories. An instance of a downstream application can be the control algorithm for a robotic hand that requires online feedback from the state of a manipulated object which would allow online grasp adaptation in a closed-loop manner. Furthermore, the tracking capacity that our method provides can help in the system identification of deforming objects in a marker-free approach. In future work, we will extend our method to more categories of objects and real world deforming point clouds
