5 papers · 1 filter
KALIKO: Kalman-Implicit Koopman Operator Learning For Prediction of Nonlinear Dynamical Systems
Albert H. Li, Ivan Dario Jimenez Rodriguez, Joel W. Burdick +2
Long-horizon dynamical prediction is fundamental in robotics and control, underpinning canonical methods like model predictive control. Yet, many systems and disturbance phenomena…
Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
Albert H. Li, Brandon Hung, Aaron D. Ames +3
Recent advancements in parallel simulation and successful robotic applications are spurring a resurgence in sampling-based model predictive control. To build on this progress, howe…
Robust Adaptive Safe Robotic Grasping with Tactile Sensing
Yitaek Kim, Jeeseop Kim, Albert H. Li +2
Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framewo…
Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer
Tyler Ga Wei Lum, Albert H. Li, Preston Culbertson +4
This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative mode…
DROP: Dexterous Reorientation via Online Planning
Albert H. Li, Preston Culbertson, Vince Kurtz +1
Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (R…