Multitask and Transfer Learning of Geometric Robot Motion

When a learning solution is needed for different robots, a model is often trained for each robot geometry, even if the robotic task is the same and the robots are structurally similar. In this paper, we address the problem of transfer learning of swept volume predictors for the motion of articulated robots with similar geometric structure. The swept volume is a scalar value corresponding to the space occupied by an entire motion of the robot. Swept volume has many applications, including being an ideal distance measure for sampling based motion planners, but it is expensive to compute. We address this learning problem through a multitask network where a common input is used to learn multiple related tasks. In this work a single network learns the kinematic-geometric information common among robots. In order to identify the properties of our multitask network favorable for transfer, we evaluate transfer properties of several shared layers, number of robots in multitask training, and feature layers. We demonstrate positive transfer results with a training set that is a fraction of the data size used in the multitask and baseline training. All the robots considered are 7-DOF manipulators with links with a variety of lengths and shapes. We also present a study of the weights and activations of the trained networks that show high correlation with the transferability patterns we observed.