Eureka: Human-level reward design via coding large language models

YJ Ma, W Liang, G Wang, DA Huang, O Bastani… - ar** reinforcement learning (RL) training
systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others, aim to …

Transferring dexterous manipulation from gpu simulation to a remote real-world trifinger

A Allshire, M MittaI, V Lodaya… - 2022 IEEE/RSJ …, 2022 - ieeexplore.ieee.org
In-hand manipulation of objects is an important capability to enable robots to carry-out tasks
which demand high levels of dexterity. This work presents a robot systems approach to …

Industreal: Transferring contact-rich assembly tasks from simulation to reality

B Tang, MA Lin, I Akinola, A Handa… - arxiv preprint arxiv …, 2023 - arxiv.org
Robotic assembly is a longstanding challenge, requiring contact-rich interaction and high
precision and accuracy. Many applications also require adaptivity to diverse parts, poses …

Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training

A Petrenko, A Allshire, G State, A Handa… - arxiv preprint arxiv …, 2023 - arxiv.org
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 …