Ashley Nathan Feniello

Reliable kinect-based navigation in large indoor environments

Practical mapping and navigation solutions for large indoor environments continue to rely on relatively expensive range scanners, because of their accuracy, range and field of view. Microsoft Kinect on the other hand is inexpensive, is easy to use and has high resolution, but suffers from high noise, shorter range and a limiting field of view. We present a mapping and navigation system that uses the Microsoft Kinect sensor as the sole source of range data and achieves performance comparable to state-of-the-art LIDAR-based systems. We show how we circumvent the main limitations of Kinect to generate usable 2D maps of relatively large spaces and to enable robust navigation in changing and dynamic environments. We use the Benchmark for Robotic Indoor Navigation (BRIN) to quantify and validate the performance of our system.

Program synthesis by examples for object repositioning tasks.

We address the problem of synthesizing human-readable computer programs for robotic object repositioning tasks based on human demonstrations. A stack-based domain specific language (DSL) is introduced for object repositioning tasks, and a learning algorithm is proposed to synthesize a program in this DSL based on human demonstrations. Once the synthesized program has been learned, it can be rapidly verified and refined in the simulator via further demonstrations if necessary, then finally executed on an actual robot to accomplish the corresponding learned tasks in the physical world. By performing demonstrations on a novel tablet interface, the time required for teaching is greatly reduced compared with using a real robot. Experiments show a variety of object repositioning tasks such as sorting, kitting, and packaging can be programmed using this approach.