5 papers
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…
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…
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…
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…
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…