Any time a robot needs to move, a motion needs to be planned. But yet roboticists often treat motion planning as a black box, and barely understand fundamental algorithms like A*. In this talk, I'll start with some intuition about search, via the absurd analogy of amoebas. I'll use the analogy to describe the first-ever edge-optimal A*-like search algorithm we invented. I'll then cast anytime search with experience (what we call the Experienced Piano Movers' Problem) as an instance of Bayesian Reinforcement Learning, enabling us to derive the first-ever sublinear regret bounds for anytime motion planning. I'll end with some open problems I want you all to solve, so I can retire in peace.
Siddhartha Srinivasa is the Boeing Endowed Professor at The Paul G. Allen School of Computer Science and Engineering at the University of Washington, and an IEEE Fellow. He is a full-stack roboticist, with the goal of enabling robots to perform complex manipulation tasks under uncertainty and clutter, with and around people. To this end, he founded the Personal Robotics Lab in 2005. He was a PI on the Quality of Life Technologies NSF ERC, DARPA ARM-S. RCTA and the DARPA Robotics Challenge, has built several robots (HERB, ADA, CHIMP, MuSHR), and has written software frameworks (OpenRAVE, DART) and best-paper award winning algorithms (CBiRRT, CHOMP, BIT*, Legibility) used extensively by roboticists around the world. Sidd received a B.Tech in Mechanical Engineering from the Indian Institute of Technology Madras in 1999, and a PhD in 2005 from the Robotics Institute at Carnegie Mellon University. He played badminton and tennis for IIT Madras, captained the CMU squash team, and lately runs competitively.