Xiaoting Dong — accepted AI-conference papers

2024

Kalman Filter-Based One-Shot Sim-to-Real Transfer Learning

Qingwei Dong, Peng Zeng, Guangxi Wan, Yunpeng He, Xiaoting Dong

RA-L 2024

Deepreinforcement learning algorithms offer a promising method for industrial robots to tackle unstructured and complex scenarios that are difficult to model. However, due to constraints related to equipment lifespan and safety requirements, acquiring a number of samples directly from the physical e

Cited by 5 Source ↗Scholar ↗

2024

Mitigating Catastrophic Forgetting in Robot Continual Learning: A Guided Policy Search Approach Enhanced With Memory-Aware Synapses

Qingwei Dong, Peng Zeng, Yunpeng He, Guangxi Wan, Xiaoting Dong

RA-L 2024

Complex operational scenarios increasingly demand that industrial robots sequentially resolve multiple interrelated problems to accomplish complex operational tasks, necessitating robots to have the capacity for not only learning through interaction with the environment but also for continual learni

Cited by 4 Source ↗Scholar ↗