Research Projects

Mobile Manipulation for the Motor Impaired

Robots for humanity was a project aimed at developing mobile manipulator robot applications for improving the quality of life of people with motor impairments by collaborating with a potential user of this technology Henry Evans, an individual with quadriplegia. My contribution enabled Henry to scratch and shave his own face using a ROS-based system of controllers and behaviors for the PR2 robot. Force-torque sensing in the wrist and current-based collision detection in the joints allowed the robot to detect unsafe conditions and react safely to avoid applying any unsafe force. By fitting a 3D ellipsoid to Henry’s head, he could use latitude-longitude control commands to move the razor naturally around his face.

Relevant Papers

Contributed Code

 

Human-Robot Collaborative Assembly

The focus of this research project was anticipating human behavior for safe and efficient human-robot collaboration in industrial environments. By tracking the human’s movement we can anticipate their future position and safely stop the robot when necessary. By tracking the hands of a person assembling one of several different constructions the robot can anticipate which bins will be needed next and deliver them without the human requesting them. The UR10 robot was mounted on a linear rail and both controlled by a real-time Linux kernel to achieve low-latency synchronized movement. I implemented ros-control drivers for both systems allowing for easy integration into the ROS system.

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Contributed Code

 

Rationally Safe Human-Robot Systems

For applications like autonomous driving, agents need not be absolutely-safe, but rather rationally-safe, that is, safe under the assumption that the human will act rationally. One of the duties of a rational agent is that they will always attempt to avoid collision, up to a reasonable level of error in control and perception. Robots should be able to leverage these assumptions to make maneuvers, like pulling out in front of a human-driven vehicle, which force the human to engage in collision-avoiding behavior. I propose modeling the interaction as a differential-game robust-control problem. As potential time to collision decreases the robot more confidently assumes the human will act to avoid collision.

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Solving Stochastic Optimal Control Problems With Feynman-Kac Forward-Backward SDEs

The Feynman-Kac theorem from stochastic control theory suggest that the Hamilton-Jacobi equations associated with the value function of stochastic optimal control problem can be solved by a pair of forward and backward stochastic differential equations (FBSDEs). Further, application of Girsanov’s theorem tells us that we can choose the forward SDE at will so long as we compensate appropriately in the backward SDE. Utilizing this surprising result we can arrive at new methods for solving optimal control problems, such as using a rapidly-exploring random tree (RRT) to represent the forward SDE. FBSDE approaches could point towards a novel method for optimal control which combines the best aspects of differential dynamic programming (DDP), cross-entropy methods, and RRTs.

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