55 citations · 115 across the 11 of their papers we have counts for
11 papers
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…
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk +5
A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However,…
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…
Symmetry Considerations for Learning Task Symmetric Robot Policies
Mayank Mittal, Nikita Rudin, Victor Klemm +2
Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively…
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Nico Gürtler, Felix Widmaier, Cansu Sancaktar +21
Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and be…
DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training
Aleksei Petrenko, Arthur Allshire, Gavriel State +2
In this work, we propose algorithms and methods that enable learning dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-ef…
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk +11
Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to learn complex robotic behaviours in simulation, including in the domain of multi-fingered…
Real Robot Challenge: A Robotics Competition in the Cloud
Stefan Bauer, Felix Widmaier, Manuel Wüthrich +39
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designe…
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo +8
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy tr…
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
Arthur Allshire, Mayank Mittal, Varun Lodaya +7
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym sim…
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning
Arthur Allshire, Roberto Martín-Martín, Charles Lin +3
The process of learning a manipulation task depends strongly on the action space used for exploration: posed in the incorrect action space, solving a task with reinforcement learni…