49 citations · 231 across the 28 of their papers we have counts for
5 papers · 2 filters
RMPflow: A Computational Graph for Automatic Motion Policy Generation
Ching-An Cheng, Mustafa Mukadam, Jan Issac +4
We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a class of reactive motion…
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,…
Robust Learning of Tactile Force Estimation through Robot Interaction
Balakumar Sundaralingam, Alexander Lambert, Ankur Handa +5
Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust…
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk +4
We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulation…
Riemannian Motion Policies
Nathan D. Ratliff, Jan Issac, Daniel Kappler +2
We introduce the Riemannian Motion Policy (RMP), a new mathematical object for modular motion generation. An RMP is a second-order dynamical system (acceleration field or motion po…