Domain Adaption as Auxiliary Task for Sim-to-Real Transfer in Vision-based Neuro-Robotic Control

Architectures for vision-based robot manipulation often utilize separate domain adaption models to allow sim-to-real transfer and an inverse kinematics solver to allow the actual policy to operate in Cartesian space. We present a novel end-to-end visuomotor architecture that combines domain adaption and inherent inverse kinematics in one model. Using the same latent encoding, it jointly learns to reconstruct canonical simulation images from randomized inputs and to predict the corresponding joint angles that minimize the Cartesian error towards a depicted target object via differentiable forward kinematics.We evaluate our model in a sim-to-real grasping experiment with the NICO humanoid robot by comparing different randomization and adaption conditions both directly and with additional real-world finetuning. Our combined method significantly increases the resulting accuracy and allows a finetuned model to reach a success rate of 80.30%, outperforming a real-world model trained with six times as much real data.