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Gaoyuan Liu

3 accepted papers

2024

Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning

RA-L 2024

Task and motion planning (TAMP) for robotics manipulation necessitates long-horizon reasoning involving versatile actions and skills. While deterministic actions can be crafted by sampling or optimizing with certain constraints, planning actions with uncertainty, i.e., probabilistic actions, remains

Cited by 2SourceScholar
2024

Where are You? Unscented Particle Filter for Single Range Relative Pose Estimation in Unobservable Motion Using UWB and VIO

RA-L 2024

Real-time relative pose (RP) estimation is a cornerstone for effective multi-agent collaboration. When conventional global positioning infrastructure such as GPS is unavailable, the use of Ultra-Wideband (UWB) technology on each agent provides a practical means to measure inter-agent range. Due to U

Cited by 1SourceScholar
2023

Synergistic Task and Motion Planning With Reinforcement Learning-Based Non-Prehensile Actions

RA-L 2023

Robotic manipulation in cluttered environments requires synergistic planning among prehensile and non-prehensile actions. Previous works on sampling-based Task and Motion Planning (TAMP) algorithms, e.g. PDDLStream, provide a fast and generalizable solution for multi-modal manipulation. However, the

Cited by 15SourceScholar