Topics
Angular Momentum Tracking
Angular momentum (AM) tracking can improve MPC by allowing the humanoid robot to more effectively maintain balance and stability during walking, running, ju...
Balance control on uneven terrain
Uneven terrain can unpredictably and shift the robot’s center of mass and instantaneously re-distribute the feet forces. Thus, it is challenging balance whil...
Centroidal dynamics approximation
Centroidal dynamics approximation can significantly reduce the complexity of the model predictive control, enabling efficient computations for real-time pred...
Emerging data-driven approaches
Data-driven approaches have become increasingly relevant in robotic control as they have achieved unprecedented success in various applications, such as slid...
Floating-base state estimation
Accurately estimating the floating-base state is critical for humanoid robots as they move freely in space, but challenges such as noisy sensory data and tig...
High-order differential dynamics
Accurate dynamics model is essential for predicting future behavior and optimizing control actions over a finite time horizon.
Whole-body model predictive control
Whole-body Model Predictive Control (MPC) can enable humanoid robots to perform complex, dynamic tasks such as walking, running, jumping, and manipulation. ...
Control design on open-source platforms
Walking within limits: Implications of Hardware in Humanoid Locomotion with Centroidal Control.
Transfer human locomotion skills
Transferring human locomotion skills to humanoid robots is a multidisciplinary research field that combines biomechanics, machine learning, and neuroscience....