With an ever-growing demand to automate different day-to-day activities, the task of autonomous manipulation using articulated robots has gained serious traction lately. In this regard, motion planning for manipulation is one of the highly researched topics. The Motion planning for manipulation is often cast as either a model-based planning problem or a machine learning problem. However, both of these [...]

Natural muscles, a result of more than 500 millions years of evolution, are elegant machines that generate force and motion electrochemically. The brief history of robotics does not have the luxury of millions of years to reverse-engineer many aspects of life. The development of artificial muscles therefore seeks to build more muscle-like actuators for robots. [...]

Radiation source localization is a common and critical task across applications such as nuclear facility decommissioning, radioactive disaster response, and security. Traditional count-based sensors (e.g. Geiger counters) infer range to the source based on the observed number of gamma photons, expected source strength, and assumed intermediate attenuation from the environment. In cluttered 3D settings, such [...]

Learning from demonstration is an intuitive approach to encoding complex behaviors in autonomous agents.  Learners have shown success in challenging tasks like autonomous driving, aerial obstacle avoidance, and information gathering, through observation and mimicry alone. State of the art algorithms like Dataset Aggregation (DAgger) have made significant advances over traditional behavior cloning, demonstrating strong theoretical [...]

Abstract: Just as humans are imperfect, even the best of robots will eventually fail at performing a task. The likelihood of failure increases as robots expand their roles in our lives. Although task failure is a common problem in robotics and human-robot interaction (HRI), there has been little research investigating human tolerance to said failures, [...]