Research hub
Every paper, policy model and hardware spec sheet indexed on RobotData, searchable in plain language. Records come from the catalog database — nothing here is a placeholder, and fields the source never reported are shown as missing.
Chi, Feng, Du et al. · Columbia / TRI / MIT · RSS 2023 · arXiv:2303.04137
Represents the visuomotor policy as a conditional denoising diffusion process over action sequences, improving average success by 46.9% over prior behavior cloning baselines.
SourceBrohan et al. · Google Robotics · RSS 2023 · arXiv:2212.06817
A 35M-parameter transformer policy trained on 130k real teleoperated episodes across 700+ kitchen tasks, showing that model capacity plus data diversity drives generalization on real hardware.
SourceBrohan et al. · Google DeepMind · CoRL 2023 · arXiv:2307.15818
Co-fine-tunes internet-scale vision-language models on robot trajectories, emitting actions as text tokens and inheriting semantic reasoning about unseen objects and instructions.
SourceOpen X-Embodiment Collaboration · 53 institutions · ICRA 2024 · arXiv:2310.08864
Pools 1M+ real robot trajectories from 22 embodiments into a single RLDS collection and shows positive transfer when training a shared policy across all of them.
SourceZhao, Kumar, Levine, Finn · Stanford · RSS 2023 · arXiv:2304.13705
Introduces ALOHA teleoperation and Action Chunking with Transformers, learning contact-rich bimanual skills from 50 demonstrations.
SourceMandlekar, Xu, Wong et al. · Stanford / NVIDIA · CoRL 2021 · arXiv:2108.03298
A large study of offline learning from proficient and mixed-quality human demonstrations, releasing the robomimic datasets and reproducible baselines.
SourceKim, Pertsch, Karamcheti et al. · Stanford / Berkeley / TRI / Google DeepMind · CoRL 2024 · arXiv:2406.09246
A 7B open VLA trained on 970k Open X-Embodiment episodes that outperforms RT-2-X with 7x fewer parameters and supports LoRA fine-tuning on consumer GPUs.
SourceOcto Model Team · Berkeley RAIL / Stanford · RSS 2024 · arXiv:2405.12213
A transformer diffusion policy pretrained on 800k Open X-Embodiment trajectories, designed for cheap fine-tuning to new sensors, action spaces and embodiments.
SourceJang, Irpan, Khansari et al. · Google / Everyday Robots · CoRL 2021 · arXiv:2202.02005
25k demonstrations plus 11k human-video interventions over 100 tasks, conditioned on language or video embeddings for zero-shot transfer to new tasks.
SourceFu, Zhao, Finn · Stanford · CoRL 2024 · arXiv:2401.02117
Extends ALOHA to a wheeled base with whole-body teleoperation and shows co-training with static ALOHA data lifts mobile manipulation success up to 90%.
SourceWalke, Black, Zhao et al. · Berkeley RAIL · CoRL 2023 · arXiv:2308.12952
60k trajectories over 24 environments on a WidowX arm, with language annotations, designed to study broad generalization for scalable imitation learning.
SourceMees, Hermann, Rosete-Beas, Burgard · University of Freiburg · RA-L 2022 · arXiv:2112.03227
A simulation benchmark of 34 tasks across four environments where policies must chain instructions from unstructured play data and raw pixels.
SourceBlack, Brown, Driess et al. · Physical Intelligence · preprint 2024 · arXiv:2410.24164
Adds a flow-matching action expert to a VLM backbone for 50Hz dexterous control, trained on a large cross-embodiment mixture plus in-house dexterous data.
SourceLiu, Zhu et al. · UT Austin · NeurIPS 2023 · arXiv:2306.03310
130 language-conditioned simulation tasks in four suites that separate spatial, object, goal and long-horizon knowledge transfer for lifelong robot learning.
SourceDasari, Ebert, Tian et al. · Berkeley / Stanford / CMU / Penn · CoRL 2019 · arXiv:1910.11215
15M video frames of self-supervised interaction across seven robot platforms, used to pretrain visual dynamics models that transfer to unseen hardware.
SourceKhazatsky, Pertsch et al. · Multi-institution consortium · RSS 2024 · arXiv:2403.12945
76k teleoperated trajectories collected in 564 real scenes by 50 operators across 13 institutions, all on the same Franka + handheld-controller rig.
SourceGu, Xiang, Li et al. · UC San Diego · ICLR 2023 · arXiv:2302.04659
20 manipulation task families with 2000+ object variations, 4M demonstration frames and a GPU-accelerated simulator running thousands of FPS.
SourceMandlekar, Nasiriany et al. · NVIDIA · CoRL 2023 · arXiv:2310.17596
Generates 50k+ synthetic demonstrations from ~200 human demos by transforming object-centric subtask segments to new scenes and objects.
SourceLynch, Wahid, Tompson et al. · Google Robotics · RA-L 2023 · arXiv:2210.06407
Releases Language-Table, the largest real-world language-conditioned manipulation dataset at the time, and a policy steerable by live natural-language guidance.
SourceHoeller, Rudin, Sako, Hutter · ETH Zurich RSL · Science Robotics 2024 · arXiv:2306.14874
A hierarchy of RL skills plus a perceptive navigation policy that lets ANYmal jump gaps, climb obstacles and traverse parkour terrain from onboard depth.
SourceHansen, Su, Wang · UC San Diego · ICLR 2024 · arXiv:2310.16828
A model-based RL algorithm that plans in a learned implicit latent space, scaling to a single 317M-parameter agent solving 80 continuous-control tasks.
SourceBharadhwaj, Vakil, Sharma et al. · CMU / Meta AI · ICRA 2024 · arXiv:2309.01918
Releases RoboSet, 100k trajectories of 12 kitchen skills, and shows semantic augmentation plus action chunking generalizes to 38 tasks from modest data.
SourceFang, Fang, Wang et al. · Shanghai Jiao Tong University · ICRA 2024 · arXiv:2307.00595
110k contact-rich robot episodes with force-torque, multi-view RGB-D and paired human demonstrations across 147 tasks and seven robot configurations.
SourceKe, Gkanatsios, Fragkiadaki · Carnegie Mellon University · CoRL 2024 · arXiv:2402.10885
Marries 3D scene tokens with diffusion policies for language-conditioned manipulation, setting new state of the art on RLBench and CALVIN.
SourceMees, Borja-Diaz, Burgard · University of Freiburg · ICRA 2023 · arXiv:2210.01911
Learns language-conditioned affordances from unstructured play data and free-form instructions, the setting behind the TACO-Play dataset.
SourceWang, Xu, Zhu et al. · MIT CSAIL · NeurIPS 2024 · arXiv:2409.20537
Aligns proprioception and vision from 52 heterogeneous datasets into a shared latent token space and shows scaling behaviour for a transferable policy trunk.
SourceLiu, Chen, Zhu et al. · Tsinghua University · ICLR 2025 · arXiv:2410.07864
A 1B-parameter diffusion transformer pretrained on 1M+ multi-robot episodes with a unified action space, then fine-tuned on 6k self-collected bimanual episodes.
SourceALOHA 2 Team · Google DeepMind · tech report 2024 · arXiv:2405.02292
A hardware revision of ALOHA with improved grippers, gravity compensation and durability, plus a MuJoCo simulation model for large-scale data collection.
SourcePertsch, Stachowicz, Ichter et al. · Physical Intelligence · preprint 2025 · arXiv:2501.09747
A DCT-based action tokenizer that compresses high-frequency action chunks, letting autoregressive VLAs train 5x faster while matching diffusion-based control quality.
SourceNVIDIA · NVIDIA GEAR · preprint 2025 · arXiv:2503.14734
A dual-system VLA for humanoids that mixes real teleoperation, synthetic neural trajectories and human video into one pretraining recipe.
SourceCadene, Alibert, Soare et al. · Hugging Face · open-source library 2024
An open library of datasets, pretrained policies and low-cost hardware designs (SO-100/SO-101) that standardizes robot learning workflows in PyTorch.
SourceAgiBot-World Contributors · AgiBot · preprint 2025 · arXiv:2503.06669
1M+ long-horizon episodes on a standardized dual-arm humanoid fleet with dexterous hands, plus the GO-1 policy baseline.
SourceKim et al. · Stanford · preprint 2025 · arXiv:2502.19645
Parallel decoding, action chunking and continuous action heads raise fine-tuned VLA throughput 25x and lift bimanual ALOHA success to 97%.
SourceShukor, Aubakirova, Capuano et al. · Hugging Face · preprint 2025 · arXiv:2506.01844
A 450M VLA trained only on community-contributed LeRobot datasets that runs on a single consumer GPU and supports asynchronous inference on low-cost arms.
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