In recent years, machine learning based approaches for autonomous robot tasks like navigation, pick-n-place, etc., have gained traction. Thanks to advances in deep learning it has become possible to train complex policies and architectures that can replace the traditional perception, planning and control pipelines that are typically found in most robot systems. But there are many open research challenges in this field, such as bridging the simulation to reality gap, understanding how to improve training, developing new power and energy-efficient policies so that machines can operate within limited energy budgets, and so forth.
Our group specializes in addressing many of these different concerns, particularly from a systems' and engineering perspective. For instance, we develop simulators for exploring end to end learning on resource-constrained platforms. Also, we focus on hardware and software co-design, such as designing energy efficient policies that are customized and tuned to the characteristics of the underlying resouce-constrained hardware. Overall, our goals are to help engineer and design the systems that can minimize the training time, understanding the computational bottlenecks to optimize the system, etc. as a whole.