103 citations · 106 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Investigating the Impact of Action Representations in Policy Gradient Algorithms
Jan Schneider, Pierre Schumacher, Daniel Häufle +2
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks. However, influences on the learning performance of RL algorithms are often poor…
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…
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…