DeepMPCVS: Deep Model Predictive Control for Visual Servoing

Pushkal Katara1    YVS Harish1    Harit Pandya3    Abhinav Gupta1    AadilMehdi Sanchawala1    Gourav Kumar2    K. Madhava Krishna1    Brojeshwar Bhowmick2   

1 IIIT Hyderabad, India    2 TCS Research and Innovation Labs, Kolkata, India    3 University of Lincoln, UK   



The simplicity of the visual servoing approach makes it an attractive option for tasks dealing with vision-based control of robots in many real-world applications. However, attaining precise alignment for unseen environments pose a challenge to existing visual servoing approaches. While classical approaches assume a perfect world, the recent data-driven approaches face issues when generalizing to novel environments. In this paper, we aim to combine the best of both worlds. We present a deep model predictive visual servoing framework that can achieve precise alignment with optimal trajectories and can generalize to novel environments. Our framework consists of a deep network for optical flow predictions, which are used along with a predictive model to forecast future optical flow. For generating an optimal set of velocities we present a control network that can be trained on-the-fly without any supervision. Through extensive simulations on photo-realistic indoor settings of the popular Habitat framework, we show significant performance gain due to the proposed formulation vis-a-vis recent state of the art methods. Specifically, we show a faster convergence and an improved performance in trajectory length over recent approaches.