Jen Jen Chung

I am an Associate Professor in the school of Electrical Engineering and Computer Science at The University of Queensland working in Robotic Perception, Planning and Learning (RPPL).

Previously, I was a senior researcher at the Autonmous Systems Lab in ETH Zürich, I was a postdoc at the Collaborative Robotics and Intelligent Systems Institute in Oregon State University and prior to all of that I completed my PhD at the Australian Centre for Field Robotics in the University of Sydney*.

Check out the latest news from RPPL Lab below, along with a list of our current and past research projects.


  • Check out our 2019 ASL Christmas video! (Short version and full version below)
  • Womenplusplus are organising Hack'n'Lead, Switzerland's first women-friendly hack-a-thon! They're also hosting a series of workshops leading up to the event that you can sign up for [here], with plenty of online material that you can try out for yourself.
  • Our paper on "Volumetric instance-aware semantic mapping and 3D object discovery" was nominated for the IROS best paper award on Cognitive Robotics! Check out the paper [here].
  • My lecture on "Learning to Coordinate" is now available on the Multi-Robot Systems Summer School website. Follow the [link] to see the recording and slides.
  • Our 2nd Workshop on Informative Path Planning and Adaptive Sampling (WIPPAS 2019) will be organized in conjunction with RSS 2019. The workshop will be held on Saturday, June 22, 2019. See you there!

Current and Past Projects

ASL, ETH Zürich

<b>Robotic Mobile Manipulation</b><p>Current robotic automation solutions typically offer 'islands of automation' where either mobility or manipulation is dealt with in isolation. Our research in this area aims to fill this gap in knowledge on combining both robotic mobility and manipulation modalities in complex, human-centred environments. Mobile manipulation tasks require robots that can interact with the world across a wide operational spectrum, from the (sub)millimetre precision required for fine manipulation to navigating across building- and campus-scale spaces. This motivates a holistic representation of the environment that facilitates the tight integration of socially aware planning, perception and control, and allows cognitive elements such as learning, reasoning and adaptation of actions for natural interaction.</p> <b>Safe Robot Navigation in Dense Crowds</b><p>Navigating through human crowds is a tough challenge for a robot. Crowds can cause severe sensor occlusions and often don't leave much free space for the robot to move in, leading to what's known as the 'freezing robot problem'. As part of the European Commisssion H2020 CrowdBot Project, we are developing navigation and motion plannng algorithms that allow the robot to work with the flow of the crowd to get to its destination. We work on mapping, localisation, prediction, motion and interaction planning to help the robot answer four key questions for successful robot crowd navigation:</p><p>1. Where am I?</p><p>2. Who's around me?</p><p>3. Where am I going?</p><p>4. How should I get there?</p>

CoRIS, Oregon State University

<b>UAV Traffic Management</b><p>UAV traffic management in urban airspaces can be formulated as a problem of routing autonomously guided robots using cost space manipulation to induce safe trajectories in the work space. Each UAV does not explicitly coordinate with other vehicles in the airspace. Instead, they each execute their own individual internal cost-based planner to travel between locations. We are developing a high-level UAV traffic management (UTM) system that can dynamically adapt the cost space to reduce the number of conflict incidents in the airspace without needing explicit knowledge of the internal planners of each UAV. Our decentralized and distributed system of high-level traffic controllers each learn appropriate costing strategies via a neuro-evolutionary algorithm. The policies learned by our algorithm demonstrated a reduction in the total number of conflict incidents experienced in the airspace while maintaining throughput performance. Current research is looking at methods to account for traffic heterogeneity in the system.</p> <b>Risk Aware Graph Search</b><p>We are investigating novel approaches to searching a graph with probabilistic edge costs, namely, by incorporating available uncertainty information into the graph search. Our proposed risk aware graph search (RAGS) method consists of two major steps, the first is to perform an initial search across the graph to find the set of non-dominated paths. Following this, we perform risk-aware planning during path execution as information of the true neighboring edge costs become available. Initial results in a graph search domain have demonstrated superior performance when compared to A*, D* and a greedy approach.</p> <b>Structural Credit Assignment in Multiagent Policy Learning</b><p>Autonomous multi-robot teams can be used in complex tasks to improve performance in terms of both speed and effectiveness. However, use of multi-robot systems presents additional challenges. Specifically, in domains where the robots' actions are coupled, coordinating multiple robots to achieve cooperative behavior at the group level is difficult. Reward shaping can greatly benefit policy learning in multi-robot tasks and we are investigating various reward frameworks based on the idea of <i>counterfactuals</i> to tackle the structural credit assignment problem in these coupled domains.</p>

ACFR, University of Sydney

<b>Learning to Soar: Resource-Constrained Exploration</b><p>In 2014 I received my Ph.D., which I completed at the Australian Centre for Field Robotics at the University of Sydney. My research focused on the development of information-based exploration strategies that can be applied within reinforcement learning frameworks to characterise the exploration-exploitation trade-off within resource-constrained learning missions. The application of interest was an unpowered aerial glider learning to soar in a wind energy field.</p> <b>Locust Swarm Tracking</b><p>The goal of this project was to gather intra-swarm locust motion data for biologists at the University of Sydney to study the effects of inter-locust interactions on the overall swarm motion. The Australian Centre for Field Robotics developed a system to collect this data consisting of micro retro-reflectors (to be attached to the insects) and a UAV equipped with a strobe beacon to autonomously fly loops over the swarm to track and monitor insect locations in real-time. Proof-of-concept was demonstrated in 2011.</p>

* Going even further back, I completed my HSC at Hurlstone Agricultural High School after graduating from Sackville Street Primary School.