193 citations · 303 across the 10 of their papers we have counts for
10 papers
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…
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Nico Gürtler, Sebastian Blaes, Pavel Kolev +5
Learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular…
Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80
Vincent Berenz, Felix Widmaier, Simon Guist +2
Robotic applications require the integration of various modalities, encompassing perception, control of real robots and possibly the control of simulated environments. While the st…
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy +7
This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipula…
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…
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…
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents
Andrea Dittadi, Frederik Träuble, Manuel Wüthrich +7
Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level…
Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation
Niklas Funk, Charles Schaff, Rishabh Madan +10
Dexterous manipulation is a challenging and important problem in robotics. While data-driven methods are a promising approach, current benchmarks require simulation or extensive en…
TriFinger: An Open-Source Robot for Learning Dexterity
Manuel Wüthrich, Felix Widmaier, Felix Grimminger +12
Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade. We argue that a hindrance is the high cost…
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Felix Grimminger, Avadesh Meduri, Majid Khadiv +10
We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor…