Theodor Chakhachiro — accepted AI-conference papers

2023

Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer

Xihang Yu, Sangli Teng, Theodor Chakhachiro, Wenzhe Tong, Tingjun Li, Tzu-Yuan Lin +4

IROS 2023poster

This paper develops a novel slip estimator using the invariant observer design theory and Disturbance Observer (DOB). The proposed state estimator for mobile robots is fully proprioceptive and combines data from an inertial measurement unit and body velocity within a Right Invariant Extended Kalman…

Cited by 19 Source ↗code ↗Scholar ↗

2023

SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning

Yifan Xu, Theodor Chakhachiro, Tribhi Kathuria, Maani Ghaffari

ICRA 2023poster

This work proposes a novel framework for socially-aware robot navigation in dynamic, crowded environments using a Deep Inverse Reinforcement Learning. To address the social navigation problem, our multi-modal learning based planner explicitly considers social interaction factors, as well as social-a…

Cited by 3 Source ↗code ↗Scholar ↗