8 citations · 14 across the 21 of their papers we have counts for
5 papers · 1 filter
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
Hrishikesh Sathyanarayan, Victor Vantilborgh, Harish Ravichandar +2
Reliable robotic manipulation requires control policies that can accurately represent and adapt to uncertainty arising from contact-rich interactions. Modern data-driven methods mi…
Learning On The Job: Zero-Shot Task Execution under Parametric Uncertainty via Trajectory-Parametrized Dual Control
Victor Vantilborgh, Hrishikesh Sathyanarayan, Guillaume Crevecoeur +2
Model-based control can achieve reliable task performance, but its effectiveness depends on the accuracy of the underlying model. Robots operating under model uncertainty must ofte…
Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation
Victor Vantilborgh, Sander De Witte, Frederik Ostyn +2
Precise identification of dynamic models in robotics is essential to support control design, friction compensation, output torque estimation, etc. A longstanding challenge remains…
A Supervisory Learning Control Framework for Autonomous & Real-time Task Planning for an Underactuated Cooperative Robotic task
Sander De Witte, Tom Lefebvre, Thijs Van Hauwermeiren +1
We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to r…
Entropy Regularised Deterministic Optimal Control: From Path Integral Solution to Sample-Based Trajectory Optimisation
Tom Lefebvre, Guillaume Crevecoeur
Sample-based trajectory optimisers are a promising tool for the control of robotics with non-differentiable dynamics and cost functions. Contemporary approaches derive from a restr…