49 citations · 231 across the 28 of their papers we have counts for
6 papers · 1 filter
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,…
Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff +1
We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that…
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…