Learning policies from expert demonstrations and human data.
Zuojin Tang, Haoyun Liu, Xinyuan Chang et al.
Vision-Language-Action (VLA) models map visual observations and language instructions to robot actions through learned latent representations.
Sandeep Bajamahal, Lawrence Yunliang Chen, Toru Lin et al.
Bimanual coordination is essential for many real-world manipulation tasks, yet learning bimanual robot policies is limited by the scarcity of bimanual robots and datasets. Single-arm robots, however,
Yuwan Liu, Hongze Yu, Song Liu et al.
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification.
Arnav Balaji, Arpit Bahety, Sriniket Ambatipudi et al.
While robotic manipulation capabilities have advanced rapidly, physical safety remains a major barrier to deploying household robots: task success is insufficient if the robot damages itself or its surroundings.
Xinyu Guo, Bin Xie, Wei Chai et al.
Vision-Language-Action (VLA) models have shown promise for generalizable robot control but struggle when adapting to new domains while preserving prior capabilities.
Matthew M. Hong, Jesse Zhang, Anusha Nagabandi et al.
Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.
Junjie Lu, Xinyao Qin, Yuhua Jiang et al.
Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distributions remains challenging.
Yuquan Xue, Le Xu, Zeyi Liu et al.
Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.
Kai Xiong, Hongjie Fang, Lixin Yang et al.
Effectively handling the interplay between spatial perception and action generation remains a critical bottleneck in robotic manipulation.
Alexander Khazatsky, Karl Pertsch, Suraj Nair et al.
A 350-hour dataset of diverse real-world robot manipulation across 22 robots in 71 scenes, designed to train scalable and generalist imitation learning policies.
Remi Cadene, Simon Alibert, Alexander Soare et al.
Hugging Face's LeRobot is an open-source PyTorch framework providing pretrained models, datasets, and training scripts for imitation and reinforcement learning on real robots, lowering the entry barrier to robot learning.
Dibya Ghosh, Homer Walke, Karl Pertsch et al.
Octo is a large open-source transformer-based generalist robot policy trained on 800k trajectories, supporting language-conditioned and goal-image-conditioned control across 9 robotic platforms with efficient fine-tuning on consumer GPUs.
Xuanlin Li, Kyle Hsu, Jiayuan Gu et al.
A benchmark environment for evaluating real-world robot manipulation policies in simulation using real-world video prompts, enabling zero-shot sim-to-real transfer evaluation without physical hardware.
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.
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.
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.
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.
Read these papers in order to build expertise in Imitation Learning.
Zuojin Tang, Haoyun Liu et al. · 2026
Sandeep Bajamahal, Lawrence Yunliang Chen et al. · 2026
Yuwan Liu, Hongze Yu et al. · 2026
Arnav Balaji, Arpit Bahety et al. · 2026
Xinyu Guo, Bin Xie et al. · 2026
Matthew M. Hong, Jesse Zhang et al. · 2026
…and 12 more papers.
