CS&E Colloquium: Towards Interactive Autonomy with Relational Reasoning

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Jiachen Li (Stanford University), will be giving a talk titled "Towards Interactive Autonomy with Relational Reasoning".

Abstract

Modern intelligent systems (e.g., autonomous vehicles, social robots) interact intensively with surrounding static/dynamic objects and human beings. In a multi-agent system, the interactions between entities/components can give rise to very complex dynamics and behavior patterns at the scales of both individuals and the entire system. Therefore, effective relational reasoning and interaction modeling among interacting entities play an essential role in scene understanding, decision making, and motion planning for autonomous systems. The ultimate goal of my research is to build intelligent and autonomous agents that can perceive, understand, and reason about the physical world; safely interact and collaborate with human beings; and efficiently coordinate with other intelligent agents. I aim to develop a unified, generalizable, and explainable framework with relational inductive biases to systematically model the relations/interactions between multiple entities/components.

In this talk, I will first discuss the formulation of relational reasoning based on a flexible and scalable graph representation, where nodes represent interacting entities and edges represent the relation between a pair of entities. The relational reasoning has been investigated from two perspectives: a) to explicitly infer the underlying relation types/patterns between entities; b) to estimate the relative importance of a certain entity with respect to another one. I will then discuss the effectiveness of relational reasoning through downstream tasks (e.g., behavior prediction, decision making). The proposed methods can be applied to multi-agent systems in various domains (e.g., physical systems, human crowds/teams, intelligent transportation systems). Finally, I will talk about my future research vision and agenda.

Biography

Dr. Jiachen Li is currently a postdoctoral scholar in the Stanford Intelligent Systems Laboratory at Stanford University working with Prof. Mykel J. Kochenderfer. Before joining Stanford, he received his Ph.D. degree in Robotics from the University of California, Berkeley working with Prof. Masayoshi Tomizuka. He was affiliated with Berkeley DeepDrive. His research interest lies at the intersection of machine learning, computer vision, reinforcement learning, control and optimization approaches, and their applications to scene understanding and decision making for intelligent autonomous systems. In particular, his research focuses on enabling effective and efficient relational learning and reasoning to model interactive behaviors for multi-agent systems in uncertain, dynamically evolving environments. He served as an organizer of multiple workshops on machine learning, computer vision, autonomous driving, and robotics at NeurIPS, ICCV, IV, ITSC, ICRA, IROS. More details can be found at https://jiachenli94.github.io/.