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.