I did my undergraduate studies at IIT Kanpur, India in Electrical Engineering, followed by a Masters in Robotics and Autonomous Systems at EPFL, Switzerland. During my Master studies, I worked with Prof. Stefan Schaal at USC and MPI Tuebingen for my Master thesis on dynamic movement primitives. I am currently a PhD student at CMU and MPI, Tuebingen. My research interests are legged robots and control of legged systems. I am particularly interested in using machine learning for controlling these robots.
In International Conference on Humanoid Robotics, pages: 512-518, IEEE, 2014, clmc (inproceedings)
Autonomous manipulation in dynamic environments is important for robots to perform everyday tasks. For this, a manipulator should be capable of interpreting the environment and planning an appropriate movement. At least, two possible approaches exist for this in literature. Usually, a planning system is used to generate a complex movement plan that satisfies all constraints. Alternatively, a simple plan could be chosen and modified with sensory feedback to accommodate additional constraints by equipping the controller with features that remain dormant most of the time, except when specific situations arise. Dynamic Movement Primitives (DMPs) form a robust and versatile starting point for such a controller that can be modified online using a non-linear term, called the coupling term. This can prove to be a fast and reactive way of obstacle avoidance in a human-like fashion. We propose a method to learn this coupling term from human demonstrations starting with simple features and making it more robust to avoid a larger range of obstacles. We test the ability of our coupling term to model different kinds of obstacle avoidance behaviours in humans and use this learnt coupling term to avoid obstacles in a reactive manner. This line of research aims at pushing the boundary of reactive control strategies to more complex scenarios, such that complex and usually computationally more expensive planning methods can be avoided as much as possible.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems