William Ziming Qu — accepted AI-conference papers

2024

Bayesian Deep Predictive Coding for Snake-like Robotic Control in Unknown Terrains

William Ziming Qu, Jessica Ziyu Qu, Li Li, Jie Yang, Yuanyuan Jia

IROS 2024

Effectively modeling the spatio-temporal interactions both internally and externally is a challenge in controlling multi-linked snake robots. This paper presents an effective method based on deep predictive coding: SnakeFormer, to address the aforementioned issue. The main contributions include: 1)

Cited by 0 Source ↗Scholar ↗

2023

Reinforcement Learning Based Multi-Layer Bayesian Control for Snake Robots in Cluttered Scenes

Jessica Ziyu Qu, William Ziming Qu, Li Li, Yuanyuan Jia

IROS 2023poster

The majority of current research on reinforcement learning (RL) for snake robot control do not sufficiently account for the spatial and temporal dependencies within the robot or its interaction with its environment during movement. To address this issue, we propose an RL based multi-layer Bayesian m…

Cited by 2 Source ↗Scholar ↗