Filippo Rozzi — accepted AI-conference papers

2024

Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation

Filippo Rozzi, Loris Roveda, Kevin Haninger

IROS 2024poster

Planning for contact-rich manipulation involves discontinuous dynamics, which presents challenges to planning methods. Sampling-based planners have higher sample complexity in high-dimensional problems and cannot efficiently handle state constraints such as force limits. Gradient-based solvers can s…

Cited by 3 Source ↗Scholar ↗

2024

Differentiable Compliant Contact Primitives for Estimation and Model Predictive Control

Kevin Haninger, Kangwagye Samuel, Filippo Rozzi, Sehoon Oh, Loris Roveda

ICRA 2024poster

Control techniques like MPC can realize contact-rich manipulation which exploits dynamic information, maintaining friction limits and safety constraints. However, contact geometry and dynamics are required to be known. This information is often extracted from CAD, limiting scalability and the abilit…

Cited by 1 Source ↗code ↗Scholar ↗