Jee-eun Lee — accepted AI-conference papers

2024

On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators

Jee-Eun Lee, Andrew Bylard, Robert Sun, Luis Sentis

ICRA 2024poster

Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained time-optimal trajectory planning (TOTP), which fo…

Cited by 3 Source ↗Scholar ↗

2023

Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

Jee-eun Lee, Jaemin Lee, Tirthankar Bandyopadhyay, Luis Sentis

ICRA 2023poster

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Ex…

Cited by 3 Source ↗Scholar ↗