Abstract: Probabilistic models have been powerful tools in modeling problems in artificial intelligence. It encodes uncertain/unknown components into stochastic substructure that works with deterministic substructure in inference problems. Markov Chain Monte Carlo (MCMC) methods follow simple designs and efficiently generate random samples that approximate a target distribution. It can provide answers to the stochastic substructure in an inference problem, which inspires us to introduce MCMC to motion planning problems.
In this talk, I will describe informed sampling methods that use MCMC in solving optimal kinodynamic motion-planning problems. Our proposed MCMC approach efficiently samples from an informed set, especially when the dimension is high and the volume of informed set gradually decreases. I will also describe how we introduce MCMC methods by directly sampling over “unknown” Pareto fronts in trajectory spaces. The sampled trajectories gradually converge to Pareto optimal solutions of a multi-objective motion planning problem.
Bio: Daqing Yi is a postdoctoral researcher working with Prof. Siddhartha Srinivasa in the Personal Robotics Lab at the University of Washington. He received his Ph.D. in Computer Science under the supervision of Prof. Michael Goodrich at Brigham Young University. He works at the intersection of robotics and interactive machine intelligence. He focuses on algorithms that bootstrap robot understanding from interaction with humans, and efficiently generate robust actions in collaborating with humans. He has received a Best Conference Paper Award from the IEEE International Conference on System, Man, and Cybernetics.
This semester of the UMass Machine Learning and Friends Lunch (MLFL) series has been graciously sponsored by our friends at Oracle Labs. MLFL is a lively and interactive forum held weekly where friends of the UMass Amherst machine learning community can sit down, have lunch, and give or hear a 50-minute presentation on recent machine learning research.
What is it? A gathering of students/faculty/staff with broad interest in the methods and applications of machine learning.
When is it? Thursdays 12:00pm to 1:00pm, unless otherwise noted. Arrive at 11:45 to get pizza.
Where is it? CS150
Who is invited? Everyone is welcome.
Is there food? Yes! Pizza is provided.
Can I present? Yes! If you would like to present your research, please email one of the organizers: Ari Kobren, Rajarshi Das, Li Yang Ku, Hang Su, Samer Nashed and Aruni Roy Chowdhury
Who generously sponsors this regular event Oracle Labs
Suggestions, comments, want to present? Contact us at email@example.com.