14 citations · 17 across the 5 of their papers we have counts for
5 papers
End-to-end RL Improves Dexterous Grasping Policies
Ritvik Singh, Karl Van Wyk, Pieter Abbeel +3
This work explores techniques to scale up image-based end-to-end learning for dexterous grasping with an arm + hand system. Unlike state-based RL, vision-based RL is much more memo…
DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands
Ritvik Singh, Arthur Allshire, Ankur Handa +2
One of the most important, yet challenging, skills for a dexterous robot is grasping a diverse range of objects. Much of the prior work has been limited by speed, generality, or re…
Synthetica: Large Scale Synthetic Data for Robot Perception
Ritvik Singh, Jingzhou Liu, Karl Van Wyk +5
Vision-based object detectors are a crucial basis for robotics applications as they provide valuable information about object localisation in the environment. These need to ensure…
Geometric Fabrics: a Safe Guiding Medium for Policy Learning
Karl Van Wyk, Ankur Handa, Viktor Makoviychuk +3
Robotics policies are always subjected to complex, second order dynamics that entangle their actions with resulting states. In reinforcement learning (RL) contexts, policies have t…
Slip Detection: Analysis and Calibration of Univariate Tactile Signals
Karl Van Wyk, Joe Falco
The existence of tactile afferents sensitive to slip-related mechanical transients in the human hand augments the robustness of grasping through secondary force modulation protocol…