6 papers
Trajectory-Regularized Stochastic Optimal Control via KL Divergence
Mintae Kim, Koushil Sreenath
We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between co…
WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
Mintae Kim, Koushil Sreenath
Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task. Offline-to-onl…
Robust Adversarial Policy Optimization Under Dynamics Uncertainty
Mintae Kim, Koushil Sreenath
Reinforcement learning (RL) policies often fail under dynamics that differ from training, a gap not fully addressed by domain randomization or existing adversarial RL methods. Dist…
RoVerFly: Robust and Versatile Implicit Hybrid Control of Quadrotor-Payload Systems
Mintae Kim, Jiaze Cai, Koushil Sreenath
Designing robust controllers for precise trajectory tracking with quadrotors is challenging due to nonlinear dynamics and underactuation, and becomes harder with flexible cable-sus…
Estimation of Aerodynamics Forces in Dynamic Morphing Wing Flight
Bibek Gupta, Mintae Kim, Albert Park +3
Accurate estimation of aerodynamic forces is essential for advancing the control, modeling, and design of flapping-wing aerial robots with dynamic morphing capabilities. In this pa…
Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots
Jiaze Cai, Vishnu Sangli, Mintae Kim +1
Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to…