103 citations · 117 across the 10 of their papers we have counts for
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion
Simon Guist, Jan Schneider, Hao Ma +9
Operating robots precisely and at high speeds has been a long-standing goal of robotics research. Balancing these competing demands is key to enabling the seamless collaboration of…
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…
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning
Simon Guist, Jan Schneider, Alexander Dittrich +3
Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort. In robotics, one approach to generate trai…