Work towards MSc degree under the supervision of Dr. Erez Karpas and Dr. Tamir Hazan
When: 2.9.2020 at 9:00
A hallmark of intelligence is the ability to deduce general principles from examples, which are correct beyond the range of those observed. Generalized Planning deals with finding such principles for a class of planning problems, so that principles discovered using small instances of a domain can be used to solve much larger instances of the same domain. In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on.
You can see the seminar here
Work towards MSc degree under the supervision of Dr. Erez Karpas
When: 20.5.2020 at 14:30
Robots operating in the real world must deal with uncertainty, be it due to working with humans who are unpredictable, or simply because they must operate in a dynamic environment.
Ignoring the uncertainty could be dangerous, while accounting for all possible outcomes, as in contingent planning, is often computationally infeasible. One possibility, which lies between ignoring the uncertainty completely and addressing it completely is to use flexible plans with choice, formulated as Temporal Planning Networks (TPNs). This approach has been successfully demonstrated to work in human-robot teaming using the Pike executive, an online executive that unifies intent recognition and plan adaptation. However, one of the main challenges to using Pike is the need to manually specify the TPN. In this work, we address this challenge by describing a technique for automatically synthesizing a TPN which covers multiple possible executions for a given temporal planning problem specified in PDDL 2.1. Our approach starts by using a diverse planner to generate multiple plans, and then merges them into a single TPN. We show how to merge the diverse plans into a single TPN using constraint optimization. An empirical evaluation on a set of IPC benchmarks shows that our approach scales well, and generates TPNs which can generalize the set of plans they are generated from.
You can see the seminar here
The graduation ceremony of the Technion Autonomous Systems Program class of 2016 was held on 13.6.2016. We are proud of you and wish you all the best.
On June the 8th 2015, the first TASP graduate students have received their MSc. We are proud of you, and wish you all the best!
The Graduate school has awarded excellence scholarships this year to four TASP students: Mr. Roei Elfassi, an M.Sc. student, and Mr. Guy Yona, a Ph.D., student received the Guthwirth Excellence Award: Mr. Tal Aharoni, an M.Sc student, received the Sherman Excellence Award: Ms. Mi La, a Ph.D student, received the Jacobs Excellence Award.
First TASP student to graduate is Mrs. Coral Moreno-Hirshfield, under the joint supervision of Prof. Pini Gurfil and Dr. Erez Ribak.