48 citations · 108 across the 8 of their papers we have counts for
8 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…
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar +4
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptat…
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,…
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
Giovanni Sutanto, Zhe Su, Stefan Schaal +1
In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are…
Learning Feedback Terms for Reactive Planning and Control
Akshara Rai, Giovanni Sutanto, Stefan Schaal +1
With the advancement of robotics, machine learning, and machine perception, increasingly more robots will enter human environments to assist with daily tasks. However, dynamically-…