Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregulated urban traffic of cars, motorcycles, buses, etc.. The rob…
Uncertainty on human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision processes (POMDPs) offer a principled framework for planning under uncertainty, often…
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents SUMMIT, a high-fidelity simulator that facilitates the develo…
We present a novel autonomous driving system which uses the road contextual information and intentions of other road users for urban driving. Unlike highways, urban environments require the drivers to follow traffic signs and signals while
Cooperative dual-crane lifting is an challenging and critical task in industrial sites. In this paper, we aim to automatically generate optimized dual-crane lifting paths under highly complex constraints, i.e., collision avoidance, coordina…
This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts for both a pedestrian’s global navigation intention and local…
Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics. The robot vehicle must contend with a dynamic and partially observable environment, noisy sensors, and many agents. A pri…
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational…
This paper presents GAMMA, a general motion prediction model that enables large-scale real-time simulation and planning for autonomous driving. GAMMA models heterogeneous, interactive traffic agents that operate under diverse road condition…
When robots operate in the real world, they need to handle uncertainties in sensing, acting, and the environment dynamics. Many tasks also require reasoning about long-term consequences of robot decisions. The partially observable Markov de…
Heavy lifting is a common and important task in industrial plants. It is conducted frequently during the time of plant construction, maintenance shutdown and new equipment installation. To find a safe and cost effective way of lifting, a te…
I am a roboticist. I am currently a Full-time Faculty at the Shanghai Innovation Institute, and an Associate Professor at the School of Artificial Intelligence, Shanghai Jiao Tong University. Prior to that, I was a postdoctoral research fel…