Background

I am a Postdoctoral Fellow in the Laboratory for Computational Sensing and Robotics at Johns Hopkins University, Department of Computer Science, where I am working jointly with Prof. Gregory Hager and Prof. Noah Cowan.

I received my Ph.D. degree in Computer Science from Rice University in July 2008. I worked jointly with Prof. Lydia Kavraki (Ph.D. advisor) and Prof. Moshe Vardi.

Research Synopsis

My research goal is to significantly increase the ability of robots to plan and act on their own or provide assistance in human-machine cooperative tasks in complex domains. Applications targeted in my research include mobile robotics, robotic-assisted surgery, robot manipulation, hybrid systems, and robotic networks.

  Synergistic Integration of AI Planning and Motion Planning

• Novel multi-layered approaches that synergistically integrate AI planning and motion planning to increase the ability of robots to plan and act on their own
• Given a high-level task, these approaches automatically plan the sequence of motions the robot needs to execute so that the resulting trajectory is dynamically feasible (even when dynamics are nonlinear), avoids collisions with obstacles, and satisfies the high-level specification
• These multi-layered approaches make it possible to plan valid continuous trajectories that satisfy high-level tasks given by sophisticated mathematical models, such as Finite State Machines, Linear Temporal Logic, and Planning Domain Definition Languages.

  Mobile Robotics

• Applications on challenging motion-planning problems with high-dimensional dynamical models of ground and aerial vehicles demonstrate speedups of orders of magnitude over related work. These improvements also make it possible to effectively incorporate physics-based simulations into planning, which further increase realism in the planned motions by modeling friction, gravity, and other interactions of the robot with the environment.

  Medical Robotics: Robotic Minimally Invasive Surgery (RMIS)

• Language of Surgery: Creating statistical models to express RMIS procedures as sentences in a structured language. Models are derived from analysis of motion and video data gathered during RMIS performed by expert surgeons on the daVinci robotic system
• Human-Machine Cooperation or Automatic Surgical Task Performance: Novel motion-planning methods that leverage the "language of surgery" models to assist surgeons in RMIS and to enable robotic systems to automatically perform surgical tasks skillfully

  Robotic Manipulation through Haptic Sensing, Exploration, and Planning

• Novel approaches for mapping and perceiving simultaneously to enable robots equipped with haptic sensors to pick up and manipulate unknown objects, purposefully explore their surfaces, and identify the objects based on geometric and tactile appearance information gathered during haptic exploration

  Hybrid Systems: Automatic Discovery of Safety Violations

• Hybrid systems go beyond continuous models by employing discrete logic to instantaneously modify the underlying dynamics and switch to a different mode in order to respond to mishaps or unanticipated changes. Hybrid systems provide sophisticated mathematical models being used in robotics, air-traffic control, and systems biology.
• Enhanced automation of air-traffic control provides a viable approach to deal with the rapid increase in air traffic and alleviate the task of human operators. Guaranteeing aircraft safety necessitates high-level specifications of conflict-resolution protocols that take into account traffic conditions. As the complexity of these protocols increases, safety verification becomes more challenging, surpassing the capabilities of current methods.
• The merit of my research lies in the ability to discover witness trajectories that demonstrate safety violations. By asking the framework to exhibit examples satisfying the negation of the safety assertion, it can be possible to verify with arbitrarily high probability that the safety assertion holds. Simulations with numerous airplanes demonstrate speedups of orders of magnitude over related work.

  Approximate Nearest Neighbors and Dimensionality Reduction

• Approaches to efficiently and accurately approximate the nearest-neighbors graph to significantly reduce the major bottleneck in nonlinear dimensionality reduction while maintaining accuracy
• Applications to biology reliably extract from molecular simulation data the main nonlinear modes of motion while reducing the time to analyze the data from several months to just a few hours

  Large-Scale High-Performance Distributed Computing

• Distributed computation of very large nearest-neighbors graphs with millions of points with hundreds of dimensions, yielding near linear speedup over hundreds of processors
• Distributed motion-planning methods providing a platform to solve high-dimensional problems with hundreds of dimensions for articulated or multi-robot systems

  Educational and Research Software