3 papers
cs.RO2025
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang, Kaiyi Ji, Junyi Geng +16
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from gene…
cs.RO2025
iKap: Kinematics-aware Planning with Imperative Learning
Qihang Li, Zhuoqun Chen, Haoze Zheng +5
Trajectory planning in robotics aims to generate collision-free pose sequences that can be reliably executed. Recently, vision-to-planning systems have gained increasing attention…
cs.RO2025
AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability
Yuheng Qiu, Can Xu, Yutian Chen +3
Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing le…