William Cong — accepted AI-conference papers

2026

Simultaneous Deep Model-Based Reinforcement Learning and State Inference under Partial Observability

ICRA 2026poster

Model-based reinforcement learning (MBRL) is a promising approach to enabling robots to learn directly from a limited number of real-world interactions. Model-based reinforcement learning (MBRL) is notoriously difficult in settings without full state observability because algorithms must simultaneou…

Cited by 0 Scholar ↗

2025

Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer

Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong, Geethika Hemkumar, Abhinav Narayan Harish +7

ICRA 2025

Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) is a promising approach to learning decisionmaking in such domains, however, end-to-end RL in complex environments is oft

Cited by 6 Source ↗Scholar ↗