I was honored to give the Lockheed Martin Robotics Seminar at the University of Maryland last month. I talked about motivation dynamics, the framework I am developing to use dynamical systems tools for autonomous task management.
[Could not find the bibliography file(s)Much of the material I discussed can be found in various publications from my group, especially [?] and [?].
Giving talks over Zoom is an art I’m still learning. Showing videos poses a particular challenge because they’re often transmitted with a lag. For those who want to take a closer look at my videos from that talk, I post them here for reference.
A group of my students was one of the fifteen teams selected to take part in the SICK LIDAR challenge https://s.sick.com/us-en-TiM10k-2019. They will receive a TIM 781 lidar sensor to develop their project to measure surface roughness in desert environments.
There was a subtle but small error in the proofs published in [?]. We have corrected the error in a new appendix G added to the arXiv version of the paper, available at https://arxiv.org/abs/1307.6134v4. These corrections also apply to other papers which built off of the results in [?], including [?], and [?].
The error arose from our application of concentration inequalities, sometimes known as tail bounds. In the originally-published proofs, we condition on the number of times that the algorithm has selected arm up to time . Since the arm selection policy depends on the rewards accrued, and the rewards are dependent random variables. In the correction, we build upon an alternative concentration inequality that accounts for this dependence and show that proofs of all the performance bounds follow a similar pattern with slight modification to the decision heuristic.
The undergraduates who worked in the lab last summer will be presenting two posters at the ASME IMECE conference based on their summer work. If you’re attending IMECE in Pittsburgh, please stop by on November 11!
Brendan Bogar will present “Investigating a Framework for Visualizing Reinforcement Learning Algorithms via Quadrupedal Robotic Simulation”.
David Chan, Mel Nguyen, Oshadha Gunasekara, and Randall Kliman will present “An object-oriented framework for fast development and testing of mobile robot control algorithms”. Abstracts are available on the IMECE website.