High precision tasks are an important part of the manufacturing industry. For example, safety constraints require that some manufacturing must be done to a very high degree of accuracy. Robotics and automation are well ...

We present an extension of a smoothing approach to Simultaneous Localization and Mapping (SLAM). We have previously introduced Square-Root SAM, a Smoothing and Mapping approach to SLAM based on Levenberg-Marquardt (LM) ...

Keyframe-based Learning from Demonstration has been shown to be an effective method for allowing end-users to teach robots skills. I propose a method for using multiple keyframe demonstrations to learn skills as sequences ...

Chinese calligraphy is unique and has great artistic value but is difficult to master. In this paper, we make robots write calligraphy. Learning methods could teach robots to write, but may not be able to ...

Reinforcement learning (RL) algorithms have successfully learned control policies for quadruped locomotion such as walking, rotation, and basic navigation. We utilize Proximal Policy Optimization and iGibson to train a ...

In this thesis, I summarize two published research papers [1][2] to which I contributed as an undergraduate researcher. My contributions to this research primarily consisted of implementing realistic human joint limitations ...

As the world's population grows older on average and the number of available caregivers decreases, assistive robotics pose an opportunity for older adults or people with disabilities to continue receiving the care that ...

How can social robotics help reduce cognitive load when humans and robots work together? This document proposes research guidelines for exploring the confluence of human-computer interaction and cognitive load theory. Field ...

State estimation is an integral part of many robotic systems. It is particularly important in the realm of high performance ground vehicles where systems must be real-time, robust, and accurate. This project aims to tackle ...

In of optimal control and reinforcement learning, the difference in the performance of a state-of-the-art policy and a mediocre one is minuscule in comparison to their difference in amortized computational cost. Further, ...