10 papers with open-source code, published in 2023.
Open X-Embodiment Collaboration
The largest collaborative robot learning dataset ever assembled, spanning 22 robots from 21 institutions, enabling training of generalist RT-X policies that exhibit positive cross-embodiment transfer.
Tony Z. Zhao, Vikash Kumar, Sergey Levine et al.
ACT employs a transformer-based action chunking policy and temporal ensembling to perform precise bimanual manipulation on a $5k ALOHA robot setup.
Cheng Chi, Siyuan Feng, Yilun Du et al.
A behavior cloning approach that models robot policies as conditional diffusion processes, enabling multimodal action distributions and smooth action sequences.
Anthony Brohan, Yevgen Chebotar, Chelsea Finn et al.
RT-1 is a 35M-parameter transformer trained on 130K robot demonstrations that generalizes to new tasks, objects, and environments, forming the foundation for Google's RT-2 and RT-X line of VLA models.
Anthony Brohan, Noah Brown, Justice Carbajal et al.
A large transformer model trained on 130k episodes of real robot manipulation to output discretized arm-and-gripper actions from RGB images and natural language instructions.
Wenlong Huang, Chen Wang, Ruohan Zhang et al.
VoxPoser uses LLMs to compose 3D value maps from free-form language instructions, enabling zero-shot closed-loop manipulation planning with model-based trajectory synthesis over dynamic environments.
Farama Foundation, Jordan Terry, Mark Towers et al.
Gymnasium is the maintained successor to OpenAI Gym, providing a standardized API for RL environments with 100+ built-in tasks, vectorized parallel execution, and native support for physics engines like MuJoCo, PyBullet, and IsaacGym.
Elia Kaufmann, Mathias Gehrig, Philipp Foehn et al.
An end-to-end neural drone controller trained entirely in simulation flies acrobatic maneuvers in the real world with zero real-world fine-tuning, enabled by domain randomization and reinforcement learning.
Jiayuan Gu, Sean Xiang, Stone Tao et al.
ManiSkill is a GPU-parallelized robotics simulation benchmark with 20+ manipulation tasks, PartNet-Mobility assets, and unified observation/action spaces, supporting RL, IL, and VLA training at millions of steps per hour.
Charlie C. Kemp, Bilsen D. Kim, Blake S. Kim et al.
A low-cost mobile manipulator designed for indoor home and office environments, with a large open-source ecosystem and teleoperation-based data collection.
