# Edward Johns

> Source: https://aiwiki.ai/wiki/edward_johns
> Updated: 2026-10-29
> Fact-checked: 2026-10-29
> Categories: Embodied AI, People, Robotics
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> Cite as: AI Wiki. "Edward Johns." aiwiki.ai, 29 Oct 2026. https://aiwiki.ai/wiki/edward_johns
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**Edward David Johns** is a British robotics researcher who is Professor of Robot Learning in the Department of Computing at Imperial College London, where he directs the Robot Learning Lab, and co-founder and CEO of the robotics startup [Machenta](https://aiwiki.ai/wiki/machenta).[1][2][25] His research focuses on robot manipulation, and much of his lab's work since 2020 has addressed how a robot can learn a new task from a single human demonstration, through one-shot [imitation learning](https://aiwiki.ai/wiki/imitation_learning) and, later, in-context imitation learning trained on simulated data.[1][3][4] Machenta, which builds on that research, came out of stealth on 27 October 2026 with a $40 million seed round co-led by Creandum and Union Square Ventures.[2][5][6]

## Education and career

Johns studied at the University of Cambridge, where he received a BA and an MEng in Electrical and Information Engineering, and then completed a PhD in computer vision at Imperial College London.[1] His doctoral thesis, "Generative methods for scene association with 2D pairwise constraints" (dated July 2013; Imperial's Spiral repository records the degree as awarded in December 2015), was supervised by Guang-Zhong Yang. It addressed recognizing which known place a query image shows, for robot navigation tasks such as loop closure, global localization and topological navigation, and its experiments used a dataset collected by walking a 7 km route through a park and urban district over eight months.[7] His papers from this period, on appearance-based place recognition and long-term localization, were co-authored with Yang and appeared at venues including ICCV 2011, ICRA 2013 and the International Journal of Computer Vision.[8]

After the PhD he spent a year as a postdoctoral researcher at University College London.[1] A CVPR 2015 paper from that period, "Becoming the Expert: Interactive Multi-Class Machine Teaching", lists him with UCL's Oisin Mac Aodha and Gabriel Brostow.[8] He then returned to Imperial as a founding member of the Dyson Robotics Lab, led by Andrew Davison, where he headed the robot manipulation team.[1] Imperial announced the lab in February 2014 as a £5 million collaboration with Dyson.[9][10]

In 2018 Johns received a Royal Academy of Engineering Research Fellowship, which his Imperial profile attributes to his work on sim-to-real transfer and imitation learning. He then joined Imperial's faculty and founded the Robot Learning Lab.[1] He was also Head of Robot Learning at Dyson from June 2021 to June 2022.[1]

| Period | Position |
|---|---|
| Before PhD | BA and MEng, Electrical and Information Engineering, University of Cambridge |
| Thesis dated July 2013 (degree awarded December 2015) | PhD, Imperial College London (supervisor Guang-Zhong Yang) |
| After PhD | Postdoctoral researcher, University College London (one year) |
| 2014 (lab founded) | Founding member of the Dyson Robotics Lab at Imperial; led its robot manipulation team |
| 1 October 2018 to 31 August 2021 | Lecturer, Department of Computing, Imperial College London |
| 28 June 2021 to 27 June 2022 | Head of Robot Learning, Dyson (Malmesbury) |
| 1 September 2021 to 31 August 2024 | Senior Lecturer, Imperial College London |
| From 1 September 2024 | Reader, Imperial College London |
| As of October 2026 | Professor of Robot Learning, Imperial College London; Director, Robot Learning Lab |
| From July 2025 | Director (company appointment 21 July 2025); co-founder and CEO, Machenta |

Sources for the table: Imperial College London profile, Spiral thesis record, Dyson Robotics Lab, Machenta and Companies House.[1][7][9][11][25] The date of his promotion from Reader to Professor is not given on his Imperial profile. Imperial's October 2026 announcement of Machenta calls him Professor of Robot Learning.[2]

## Research

The Robot Learning Lab describes its research as lying at the intersection of robotics, [computer vision](https://aiwiki.ai/wiki/computer_vision) and machine learning, with a primary focus on [robot manipulation](https://aiwiki.ai/wiki/robot_manipulation). It names imitation learning, [reinforcement learning](https://aiwiki.ai/wiki/reinforcement_learning) and [vision-language models](https://aiwiki.ai/wiki/vision_language_model) as its main lines of investigation, with applications in domestic, manufacturing and warehouse robots.[3] Johns's Imperial profile says he has published more than 80 peer-reviewed papers with more than 8,000 citations.[1]

### Early work: grasping and sim-to-real transfer

At the Dyson Robotics Lab, Johns worked on learning robot skills from simulated data. "Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty" (IROS 2016, with Stefan Leutenegger and Andrew Davison) trained a CNN on grasp attempts collected in simulation, and "Pairwise Decomposition of Image Sequences for Active Multi-View Recognition" (CVPR 2016 oral) addressed multi-view object recognition.[4] In "Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task" (CoRL 2017), Stephen James, Davison and Johns trained a controller to grasp a cube and drop it into a basket using behavioral cloning in simulation, then transferred it to a real robot with domain randomization.[4][12] Later lab papers benchmarked [sim-to-real transfer](https://aiwiki.ai/wiki/sim_to_real_transfer) for dynamics (IROS 2020) and domain randomization for visual pose estimation (ICRA 2021).[4]

### One-shot imitation learning

From 2021 the lab published a series of methods that learn a manipulation task from a single demonstration. Johns's single-author ICRA 2021 paper, "Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration", splits a task into a coarse approach trajectory and a fine interaction trajectory. A state estimator for the pose at which interaction begins is trained by [self-supervised learning](https://aiwiki.ai/wiki/self_supervised_learning), with the robot moving its wrist camera around the object; at test time the robot moves to that pose and replays the demonstration's end-effector velocities. The paper reports experiments on 8 everyday tasks.[13] Machenta's team page calls a 2021 self-supervised learning paper the one "that started it all".[11]

DOME ("Demonstrate Once, Imitate Immediately", IROS 2022) needs no data collection after the demonstration. It uses an object segmentation network and a visual servoing network, trained in advance in simulation, so that a task can be performed straight after one demonstration with no further data collection or training; the authors report near 100% success on 7 real-world tasks.[4][14] Later work studied the problem from a pose estimation perspective (CoRL 2023), used DINO vision features to retrieve and align demonstrations for novel objects (DINOBot, ICRA 2024; see [DINOv2](https://aiwiki.ai/wiki/dinov2)), and extended one-shot learning to dual-arm tasks (ICRA 2025).[4][15]

In a CoRL 2021 blue sky paper, "Back to Reality for Imitation Learning", Johns argued that evaluation of real-world robot learning should focus on time efficiency, which better reflects the cost to humans, rather than the data efficiency metrics inherited from machine learning.[4][16]

### In-context imitation learning

The lab's later work treats demonstrations as a prompt for a pre-trained model, a robotics counterpart to [in-context learning](https://aiwiki.ai/wiki/in_context_learning) in [large language models](https://aiwiki.ai/wiki/large_language_model). "Few-Shot In-Context Imitation Learning via Implicit Graph Alignment" (CoRL 2023, with Vitalis Vosylius) generated large numbers of virtual demonstrations by deforming random objects in simulation.[4] "Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics" (RSS 2024, with Norman Di Palo) represented observations and actions as 3D keypoints so that demonstrations could be fed to a large language model, using the model's pattern recognition for low-level action prediction rather than high-level reasoning in natural language.[4][17]

"Instant Policy: In-Context Imitation Learning via Graph Diffusion" (Vosylius and Johns) learns new tasks from one or two demonstrations without further training. It represents demonstrations, observations and actions as a graph, generates actions with a learned [diffusion](https://aiwiki.ai/wiki/diffusion_model) process, and is trained on "pseudo-demonstrations": arbitrary trajectories generated in simulation.[18] It was presented as an oral at ICLR 2025 and, according to the lab, won the Best Paper Award at the ICLR 2025 Robot Learning Workshop.[3][19]

"Learning a Thousand Tasks in a Day" (Kamil Dreczkowski, Pietro Vitiello, Vosylius and Johns), published in Science Robotics in 2025, compared ways of decomposing manipulation into alignment and interaction phases across 3,450 real-world rollouts. The resulting method, Multi-Task Trajectory Transfer (MT3), learns tasks from as little as one demonstration each; the paper reports teaching a robot 1,000 distinct everyday tasks in under 24 hours of human demonstrator time.[20] The lab says the paper appeared on the journal's front cover.[3] Machenta names Instant Policy and Learning a Thousand Tasks in a Day as the two 2025 results that triggered the company's founding.[11]

### Language and generative models

The lab has also worked with web-scale generative models. DALL-E-Bot (RA-L 2023) inferred a text description of a scene, prompted [DALL-E](https://aiwiki.ai/wiki/dall_e_2) to generate a goal image, and rearranged objects to match it; the lab calls it the first work to study web-based diffusion models for robotics.[4][21] Dream2Real (ICRA 2024) built a [NeRF](https://aiwiki.ai/wiki/nerf) of a scene, rendered candidate rearrangements and scored them against a language command with [CLIP](https://aiwiki.ai/wiki/clip).[4] Johns is a co-author of Prismer (TMLR 2024), a vision-language model with multi-task experts, with researchers including Linxi Fan and Anima Anandkumar.[4]

Johns is one of the many co-authors of "Open X-Embodiment: Robotic Learning Datasets and RT-X Models", the cross-institution [Open X-Embodiment](https://aiwiki.ai/wiki/open_x_embodiment) dataset paper, which won the Best Conference Paper Award at ICRA 2024.[22]

### Selected publications

| Year | Title | Authors | Venue |
|---|---|---|---|
| 2016 | Pairwise Decomposition of Image Sequences for Active Multi-View Recognition | Johns, Leutenegger, Davison | CVPR 2016 (oral) |
| 2017 | Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task | James, Davison, Johns | CoRL 2017 |
| 2021 | Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration | Johns | ICRA 2021 |
| 2021 | Back to Reality for Imitation Learning | Johns | CoRL 2021 (blue sky oral track) |
| 2022 | Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning | Valassakis, Papagiannis, Di Palo, Johns | IROS 2022 |
| 2023 | DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics | Kapelyukh, Vosylius, Johns | IEEE RA-L 2023 |
| 2023 | One-Shot Imitation Learning: A Pose Estimation Perspective | Vitiello, Dreczkowski, Johns | CoRL 2023 |
| 2023 | Few-Shot In-Context Imitation Learning via Implicit Graph Alignment | Vosylius, Johns | CoRL 2023 |
| 2024 | DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models | Di Palo, Johns | ICRA 2024 |
| 2024 | Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics | Di Palo, Johns | RSS 2024 |
| 2024 | MILES: Making Imitation Learning Easy with Self-Supervision | Papagiannis, Johns | CoRL 2024 |
| 2025 | R+X: Retrieval and Execution from Everyday Human Videos | Papagiannis, Di Palo, Vitiello, Johns | ICRA 2025 |
| 2025 | Instant Policy: In-Context Imitation Learning via Graph Diffusion | Vosylius, Johns | ICLR 2025 (oral) |
| 2025 | Learning a Thousand Tasks in a Day | Dreczkowski, Vitiello, Vosylius, Johns | Science Robotics, vol. 10, eadv7594 |
| 2026 | Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation | Wang, Qian, Johns | CoRL 2026 (accepted) |
| 2026 | ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim | Kapelyukh, Zhang, James, Herlant, Johns | IEEE RA-L 2026 |

Sources for the table: the Robot Learning Lab publication list and the arXiv records of each paper.[4][12][13][14][15][16][17][18][20][21][23][26]

## Machenta

Johns co-founded Machenta with Vosylius, his former PhD student, who is the company's CTO.[6][11] Companies House records Johns's appointment as a director of Machenta Ltd on 21 July 2025.[25] Imperial says the two founded it with colleagues from the Robot Learning Lab to scale the lab's work.[2][6] The company says every author of the five lab papers it lists as its research lineage (from the 2021 self-supervised learning paper to Instant Policy and Learning a Thousand Tasks in a Day) now works at Machenta.[11] Business Insider reported at launch that the company had 11 employees and was focused on research, with no near-term plans to commercialize its technology.[6]

Johns announced the company on X on 27 October 2026, together with the $40 million seed round co-led by Creandum and Union Square Ventures.[5] Machenta describes its model as a "vision-demo-action" model that learns a new task from one demonstration with no further training and was trained entirely in simulation; it calls the capability "Instant Robot Learning", and Johns described teaching robots with it as "physical vibe coding". These are the company's claims, shown through its own videos.[5][24] Imperial described the round as the largest seed raise by a UK robotics startup.[2] Speaking to Imperial at the launch, Johns said: "Intelligence is not so much about what you can already do, it's about how quickly you can learn to do something new."[2]

## Awards and recognition

| Year | Award | Source |
|---|---|---|
| 2018 | Royal Academy of Engineering Research Fellowship | Imperial profile[1] |
| 2022 | Imperial College President's Award for Outstanding Early Career Researcher | Imperial profile[1] |
| 2023 | UK-RAS Early Career Award | Imperial profile[1] |
| 2024 | ICRA 2024 Best Conference Paper Award, as a co-author of the Open X-Embodiment paper | ICRA 2024[22] |
| 2025 | Best Paper Award, ICLR 2025 Robot Learning Workshop (Instant Policy, with Vosylius) | Robot Learning Lab[3] |

His Imperial profile also says he has served on the advisory boards of several robotics and AI startups.[1]

## References

1. Imperial College London, "Professor Edward Johns", staff profile (bio, academic and non-academic positions, degrees). Accessed 30 October 2026. https://profiles.imperial.ac.uk/e.johns
2. David Silverman, "Imperial AI spinout Machenta raises $40 million to make robots instant learners", Imperial College London News, 27 October 2026. https://www.imperial.ac.uk/news/articles/2026/imperial-ai-spinout-machenta-raises-40-million-to-make-robots-instant-learners/
3. The Robot Learning Lab at Imperial College London, home page and latest news. Accessed 30 October 2026. https://www.robot-learning.uk/
4. The Robot Learning Lab at Imperial College London, "Our Research" (publication list with mini abstracts). Accessed 30 October 2026. https://www.robot-learning.uk/research
5. Edward Johns (@Ed__Johns), announcement of Machenta and its $40M seed round, X, 27 October 2026. https://x.com/Ed__Johns/status/2115030792720031996
6. Rya Jetha, "An 11-person startup raised $40 million to challenge robotics' data-hungry giants", Business Insider, 27 October 2026. https://www.businessinsider.com/this-11-person-startup-raised-40-million-train-robots-virtual-worlds-2026-10
7. Edward David Johns, "Generative methods for scene association with 2D pairwise constraints", PhD thesis, Department of Computing, Imperial College London, 2013 (advisor Guang-Zhong Yang), Spiral repository, doi:10.25560/33210. http://hdl.handle.net/10044/1/33210
8. OpenAlex, author record for Edward Johns (ORCID 0000-0002-8914-8786), works list. Accessed 30 October 2026. https://openalex.org/A5080385974
9. Imperial College London, Dyson Robotics Lab, "About us". Accessed 30 October 2026. https://www.imperial.ac.uk/dyson-robotics-lab/about-us/
10. Imperial College London, "Dyson and Imperial to develop next generation robots at new centre", 10 February 2014. https://www.imperial.ac.uk/news/142147/dyson-imperial-develop-next-generation-robots/
11. Machenta, "Team". Accessed 30 October 2026. https://machenta.com/team
12. Stephen James, Andrew J. Davison and Edward Johns, "Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task", CoRL 2017, arXiv:1707.02267. https://arxiv.org/abs/1707.02267
13. Edward Johns, "Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration", ICRA 2021, arXiv:2105.06411. https://arxiv.org/abs/2105.06411
14. Eugene Valassakis, Georgios Papagiannis, Norman Di Palo and Edward Johns, "Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning", IROS 2022, arXiv:2204.02863. https://arxiv.org/abs/2204.02863
15. Norman Di Palo and Edward Johns, "DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models", ICRA 2024, arXiv:2402.13181. https://arxiv.org/abs/2402.13181
16. Edward Johns, "Back to Reality for Imitation Learning", CoRL 2021 blue sky oral track, arXiv:2111.12867. https://arxiv.org/abs/2111.12867
17. Norman Di Palo and Edward Johns, "Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics", RSS 2024, arXiv:2403.19578. https://arxiv.org/abs/2403.19578
18. Vitalis Vosylius and Edward Johns, "Instant Policy: In-Context Imitation Learning via Graph Diffusion", arXiv:2411.12633. https://arxiv.org/abs/2411.12633
19. The Robot Learning Lab at Imperial College London, "Instant Policy" project page. Accessed 30 October 2026. https://www.robot-learning.uk/instant-policy
20. Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius and Edward Johns, "Learning a Thousand Tasks in a Day", Science Robotics 10, eadv7594 (2025), doi:10.1126/scirobotics.adv7594; arXiv:2511.10110. https://arxiv.org/abs/2511.10110
21. Ivan Kapelyukh, Vitalis Vosylius and Edward Johns, "DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics", IEEE RA-L, arXiv:2210.02438. https://arxiv.org/abs/2210.02438
22. ICRA 2024, "Awards and Finalists" (IEEE ICRA Best Conference Paper Award winners). Accessed 30 October 2026. https://2024.ieee-icra.org/awards-and-finalists/
23. Yilong Wang, Cheng Qian and Edward Johns, "Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation", accepted at CoRL 2026, arXiv:2606.04269. https://arxiv.org/abs/2606.04269
24. Machenta, "Introducing Instant Robot Learning", Machenta Blog, 27 October 2026. https://machenta.com/blog/instant-robot-learning
25. Companies House, "MACHENTA LTD (16594535): officers". Accessed 30 October 2026. https://find-and-update.company-information.service.gov.uk/company/16594535/officers
26. Ivan Kapelyukh, Xiaohan Zhang, Stephen James, Laura Herlant and Edward Johns, "ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim", IEEE RA-L 2026, arXiv:2609.34010. https://arxiv.org/abs/2609.34010