Xinda Qi — accepted AI-conference papers

2024

Back-Stepping Experience Replay With Application to Model-Free Reinforcement Learning for a Soft Snake Robot

Xinda Qi, Dong Chen, Zhaojian Li, Xiaobo Tan

RA-L 2024

In this letter, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms. BER aims to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. T

Cited by 7 Source ↗Scholar ↗

2020

A Novel Pneumatic Soft Snake Robot Using Traveling-Wave Locomotion in Constrained Environments

Xinda Qi, Hongyang Shi, Thassyo Pinto, Xiaobo Tan

RA-L 2020

In this paper we propose a novel pneumatic soft snake robot which exploits traveling-wave motion to move in complex, constrained environments such as a pipeline. The robot is modular, with a unique pneumatic system design that requires the use of only four air channels regardless of the number of mo

Cited by 84 Source ↗Scholar ↗