Junhong Xu
Vehicle Autonomy and Intelligence Lab at Indiana University, Bloomington.
About Me
I am a fifth-year Ph.D. candidate at Vehicle Autonomy and Intelligence Lab at Indiana University, Bloomington. My research focuses on decision-making under uncertainty for real-world robotic applications. I am particularly interested in developing practical and scalable decision-making algorithms (a.k.a. stochastic control – I deliberately omitted “optimal” as it is generally difficult to find an optimal solution for many interesting real-world problems) that consider the following commonly existing uncertainties in real-world robotic systems:
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motion outcome uncertainty, where the robot cannot accurately predict its motion outcome after executing a control command,
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interaction uncertainty, where the behaviors of other interacting agents are uncertain,
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state uncertainty, where the physical state of the robot cannot be accurately determined (e.g., localization error),
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perception uncertainty, where the outcome of a perception model is uncertain (e.g., object class cannot be predicted with certainty due to occlusion).
Prior to my Ph.D., I also worked on reducing DAgger’s manual labeling in imitation learning and multi-task imitation learning for mobile robot navigation in indoor environments.
News
Oct, 2022 | Our paper "Causal Inference for De-biasing Motion Estimation from Robotic Observational Data" on leveraging causal inference to learn robot motion model is on arxiv (submitted to ICRA 2023). |
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Sep, 2022 | Our paper "Decision-Making Among Bounded Rational Agents" on explicit modeling of computational limits in multi-agent motion planning using information-theoretic bounded rationality is accepted by DARS 2022. |
Oct, 2021 | Our team is 1 of 4 teams selected (others are MIT, CMU and University of Washington) by the Army Research Laboratory (ARL) to enhance unmanned ground vehicle’s navigation and control capability in off-road environments. |
Nov, 2020 | We successfully deployed our decision-making under uncertainty planner to off-road environments. |
Sep, 2020 | Our paper “Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles” is accepted by IROS 2020. |
Jul, 2020 | Our paper “State-Continuity Approximation of Markov Decision Processes via Finite Element Methods for Autonomous System Planning” is accepted by R-AL. |
Jun, 2020 | Our paper "Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes" on developing scalable algorithm for decision-making under uncertainty problems is accepted in Robotic: Science and Systems. |
Selected Publications
- Decision-Making Among Bounded Rational AgentsDistributed Autonomous Robotic Systems 2022
- Causal Inference for De-biasing Motion Estimation from Robotic Observational DataarXiv preprint arXiv:2210.08679 2022
- Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision ProcessesRobotics: Science and System 2020
- Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile VehiclesIn 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
- State-continuity approximation of markov decision processes via finite element methods for autonomous system planningIEEE Robotics and Automation Letters 2020
- Reachable space characterization of markov decision processes with time variabilityRobotics: Science and Systems 2019
- Automated labeling for robotic autonomous navigation through multi-sensory semi-supervised learning on big dataIEEE Transactions on Big Data 2019
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- A deep residual convolutional neural network for facial keypoint detection with missing labelsSignal Processing 2018