Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation

M Heo, Y Lee, D Lee, JJ Lim - The International Journal of …, 2023 - journals.sagepub.com
Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP)
have demonstrated impressive performance across various robotic manipulation tasks …

Robel: Robotics benchmarks for learning with low-cost robots

M Ahn, H Zhu, K Hartikainen, H Ponte… - … on robot learning, 2020 - proceedings.mlr.press
ROBEL is an open-source platform of cost-effective robots designed for reinforcement
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …

Trifinger: An open-source robot for learning dexterity

M Wüthrich, F Widmaier, F Grimminger, J Akpo… - ar**
L Yang, F Wan, H Wang, X Liu, Y Liu… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
Robot learning is widely accepted by academia and industry with its potentials to transform
autonomous robot control through machine learning. Inspired by widely used soft fingers on …

Robotic manipulation datasets for offline compositional reinforcement learning

M Hussing, JA Mendez, A Singrodia, C Kent… - arxiv preprint arxiv …, 2023 - arxiv.org
Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train
on large datasets, avoiding the recurrence of expensive data collection. To advance the …

Reinforcement learning experiments and benchmark for solving robotic reaching tasks

P Aumjaud, D McAuliffe, FJ Rodríguez-Lera… - Workshop of Physical …, 2020 - Springer
Reinforcement learning has shown great promise in robotics thanks to its ability to develop
efficient robotic control procedures through self-training. In particular, reinforcement learning …