4 citations · 8 across the 3 of their papers we have counts for
5 papers
Differentiable and Learnable Robot Models
Franziska Meier, Austin Wang, Giovanni Sutanto +2
Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics…
Learning Equality Constraints for Motion Planning on Manifolds
Giovanni Sutanto, Isabel M. Rayas Fernández, Peter Englert +2
Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations wi…
Learning Manifolds for Sequential Motion Planning
Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert +2
Motion planning with constraints is an important part of many real-world robotic systems. In this work, we study manifold learning methods to learn such constraints from data. We e…
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
Giovanni Sutanto, Austin S. Wang, Yixin Lin +4
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots. RNEA can be framed as a differentiable computational graph, enabling the dy…
Learning Latent Space Dynamics for Tactile Servoing
Giovanni Sutanto, Nathan Ratliff, Balakumar Sundaralingam +4
To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper,…