Ziheng Ji — accepted AI-conference papers

2026

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

Ziyu Shan, Yuheng Zhou, Gaoyuan Wu, Ziheng Ji, Zhenyu Wu, Ziwei Wang

RA-L 2026

Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage paradigm, where the robot first navigates to a docking point and then performs fixed-base manipulation using powerful vis

Cited by 0 Source ↗code ↗Scholar ↗

2024

ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning

Changze Li, Ziheng Ji, Zhe Chen, Tong Qin, Ming Yang

IROS 2024

Autonomous parking is a crucial task in the intelligent driving field. Traditional parking algorithms are usually implemented using rule-based schemes. However, these methods are less effective in complex parking scenarios due to the intricate design of the algorithms. In contrast, neural-network-ba

Cited by 18 Source ↗code ↗Scholar ↗