Learning policies from expert demonstrations and human data.
Zuojin Tang, Haoyun Liu, Xinyuan Chang et al. · arXiv · May 2026
Vision-Language-Action (VLA) models map visual observations and language instructions to robot actions through learned l...
Sandeep Bajamahal, Lawrence Yunliang Chen, Toru Lin et al. · ICRA 2026 · May 2026
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. · arXiv preprint · Jul 2026
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification. Prior work has incorporated ...
Arnav Balaji, Arpit Bahety, Sriniket Ambatipudi et al. · RSS 2026 · Jun 2026
While robotic manipulation capabilities have advanced rapidly, physical safety remains a major barrier to deploying household robots: task success is insufficient if the robot dama...
Xinyu Guo, Bin Xie, Wei Chai et al. · arXiv · May 2026
Vision-Language-Action (VLA) models have shown promise for generalizable robot control but struggle when adapting to new...
Matthew M. Hong, Jesse Zhang, Anusha Nagabandi et al. · arXiv · May 2026
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...
Junjie Lu, Xinyao Qin, Yuhua Jiang et al. · arXiv · May 2026
Adapting Vision-Language-Action (VLA) models to specific user preferences or task requirements typically requires expens...
Yuquan Xue, Le Xu, Zeyi Liu et al. · arXiv preprint · Jul 2026
Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond t...
Kai Xiong, Hongjie Fang, Lixin Yang et al. · arXiv · May 2026
Effectively handling the interplay between spatial perception and action generation remains a critical bottleneck in robotic manipulation. Existing methods typically treat spatial perception and action execution as decoupled or strictly...
Alexander Khazatsky, Karl Pertsch, Suraj Nair et al. · RSS 2024 · Jul 2024
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. · NeurIPS 2024 Workshop · Jun 2024
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. · RSS 2024 · Jul 2024
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. · RSS 2024 · Jul 2024
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. · RSS 2023 · Jul 2023
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. · RSS 2023 · Jul 2023
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. · ICRA Workshop · 2023
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 · arXiv · Oct 2023
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. · RSS · Jul 2023
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.
