6 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…
Stein Variational Uncertainty-Adaptive Model Predictive Control
Hrishikesh Sathyanarayan, Ian Abraham
We propose a Stein variational distributionally robust controller for nonlinear dynamical systems with latent parametric uncertainty. The method is an alternative to conservative w…
Dual Control Reference Generation for Optimal Pick-and-Place Execution under Payload Uncertainty
Victor Vantilborgh, Hrishikesh Sathyanarayan, Guillaume Crevecoeur +2
This work addresses the problem of robot manipulation tasks under unknown dynamics, such as pick-and-place tasks under payload uncertainty, where active exploration and(/for) onlin…
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…
Behavior Synthesis via Contact-Aware Fisher Information Maximization
Hrishikesh Sathyanarayan, Ian Abraham
Contact dynamics hold immense amounts of information that can improve a robot's ability to characterize and learn about objects in their environment through interactions. However,…
Exciting Contact Modes in Differentiable Simulations for Robot Learning
Hrishikesh Sathyanarayan, Ian Abraham
In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experi…