EE Seminar: Sense-Plan-Act in the age of Deep Learning

(The talk will be given in English)

Speaker:     Prof. Aviv Tamar

                        Electrical and Computer Engineering department, Technion

Monday, May 12^th, 2025

13:00 - 14:00

Sense-Plan-Act in the age of Deep Learning

Abstract

A central paradigm in autonomous robots is the "sense-plan-act" loop, where at each time step the robot observes the environment, plans an appropriate action, executes it, and continues to the next observation. This talk focuses on the role of deep neural networks in this paradigm. I will first cover several works on learning world models for robotic manipulation, starting with the Causal InfoGAN model for rope manipulation, and more recent work based on our Deep Latent Particles (DLP) model. Here, neural networks are used to learn a model of the environment, which can be used for planning, or for directly learning a policy. The second part of the talk will focus on using neural networks to speed up a planning algorithm (e.g., tree search), based on our recent work on Bayesian Online Planning. The main idea is that neural networks can be seen as approximate posteriors in a Bayesian formulation of tree search that we propose. In this formulation, the *uncertainty* of the neural network prediction is automatically accounted for, and can be exploited for faster search.

Short Bio

Aviv Tamar is an associate professor at the Electrical and Computer Engineering department at Technion. His work focuses on reinforcement learning and robot learning. Aviv is the recipient of the Krill prize, an ERC starting grant, and best paper awards at NeurIPS and NSDI.

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