20 citations · 41 across the 3 of their papers we have counts for
3 papers
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…
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…