4 papers
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…
Quality Over Quantity: Curating Contact-Based Robot Datasets Improves Learning
Hrishikesh Sathyanarayan, Victor Vantilborgh, Ian Abraham
In this paper, we investigate the utility of datasets and whether more data or the 'right' data is advantageous for robot learning. In particular, we are interested on quantifying…
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…