A. Irpan

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 12 papers; trusts 371 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 376 citations into it; #16,454 of 35,345 by trust here, #2,101 by citations
• Computer Vision and Pattern Recognition — 307 citations into it; #6,076 of 21,575 by trust here, #1,315 by citations
• Reinforcement Learning — 231 citations into it; #849 of 2,242 by trust here, #176 by citations
• Control and Systems Engineering — 158 citations into it; #1,987 of 8,257 by trust here, #65 by citations
• Robotics & Control — 94 citations into it; #588 of 609 by trust here, #133 by citations
• Natural Language Processing — 69 citations into it
• Representation & Self-Supervised Learning — 48 citations into it
• Biomedical Engineering — 44 citations into it; #4,670 of 10,734 by trust here, #1,313 by citations

Whom A. Irpan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Sergey Levine
4. Alex Krizhevsky
5. Kaiming He
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Chelsea Finn

Who trusts A. Irpan overall

1. Sergey Levine
2. Sergey I. Nikolenko
3. Chelsea Finn
4. Justin Fu
5. Aviral Kumar
6. Ted Xiao
7. Han-Jia Ye
8. Ofir Nachum
9. Julian Ibarz
10. David R Ha

Papers from A. Irpan's perspective

Ordered by how strongly the researchers A. Irpan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (2018) — 1,735 citations
• Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018) — 731 citations
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) — 3,376 citations
• RT-1: Robotics Transformer for Real-World Control at Scale (2022) — 2,545 citations
• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning (2022) — 813 citations
• Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks (2018) — 508 citations
• Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills (2021) — 178 citations
• Off-Policy Evaluation via Off-Policy Classification (2019) — 56 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Meta-Learning Requires Meta-Augmentation (2020) — 110 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023) — 3,674 citations