Ingmar Posner
@IngmarPosner
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Applied Machine Learner, Roboticist, Professor at University of Oxford.
Joined February 2017
Our contributions at #CoRL2025 👇 Main Conference: - D-Cubed: https://t.co/B6IhtLwVT6 - COMBO-Grasp: https://t.co/m1VZzSfARR GenPrior workshop: - Guaranteed SE(3) Equivariant Control:
openreview.net
This paper addresses the challenge of occluded robot grasping, i.e. grasping in situations where the desired grasp poses are kinematically infeasible due to environmental constraints such as...
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Always look forward to CoRL - for the people, papers & workshops. Sadly have to miss it this year. Delighted that @junjungoal & Alex Mitchell are there representing A2I - and @JankowskiJulius is presenting new work from our Amazon team! 🎉 See you next year!
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NVIDIA’s £2bn pledge to UK AI startups is recognition that we don’t just use AI: we invent it, shape it & apply it. As @UniofOxford researcher & co-founder of a successful AI startup, I was proud to be in the room. #AI #UKTech
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Had a great time sharing @a2i_oxford’s work on generative AI for robot & world models at the @royalsociety #SummerScience Exhibition 2025. Great energy, excellent questions! 🎥 Watch here: https://t.co/eVdA2lHVjX
@oxfordrobots @oxengsci #RobotLearning #GenerativeAI #Robotics
royalsociety.org
Join Professor Ingmar Posner to discover how robots are learning to learn and think — and why the future of intelligent machines is anything but robotic.
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🎓 Multiple faculty positions @oxengsci ! 🎓 We welcome applications from outstanding candidates in robotics, and especially if you are working in areas such as human-robot interaction, mechanical design, novel robotic sensor design and/or field robotics. Closing soon...🚀
Multiple faculty positions at University of Oxford in @oxengsci - Join Us! Robotics - Computer Vision - Machine Learning Faculty positions in Oxford are typically linked to a college. Please repost!
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Amongst my favourite research directions this year: understanding model complexity and its link to generalization and intelligence. Progress here could mean leaner models, versatile representations, and less reliance on data/energy. Excited that we’re off to the races on this!
I’m pleased to announce our work which studies complexity phase transitions in neural networks! We track the Kolmogorov complexity of networks as they “grok”, and find a characteristic rise and fall of complexity, corresponding to memorization followed by generalization. 🧵
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2️⃣ RAINZ CDT (w/ @UKAEAofficial): Robot manipulation for net-zero energy systems (assembly/disassembly focus). 🗓 Deadline: 31 Jan 2025 👉
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🚀 Funded PhD Studentship Alert! 🚀 Join @a2i_oxford in our mission to advance World Models for robotics and beyond. Two opportunities starting Oct 2025. Deadlines in January 2025. Details in 🧵… @oxfordrobots 🤖 #Robotics #ArtificialInteligence #MachineLearning
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How can a transformer uncover local causal dependencies in dynamic systems, from simulations to real-world data? 🤔 The answer: Hard attention + sparsity. But with a twist. Meet SPARTAN: More causal. More efficient. Just as accurate. #Robotics #ML #AI #CausalAI
Very excited to share our new work - SPARTAN: A Sparse Transformer Learning Local Causation. We develop a Transformer world model that learns local causal dependencies between entities, leading to improved adaptation efficiency and robustness with accurate prediction. 🧵
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🚀 Join us to push the boundaries of AI and robotics, working on cutting-edge research in real-world robot learning. You'll focus on developing multimodal world models with impactful applications in collaborative manufacturing and social care.🤖 #robotlearning #GenerativeAI
🚀 We’re hiring a Postdoc in Multimodal World Modelling for robot skill acquisition! 🌟 Do you have a passion for deploying AI on real-world robots? Then this one may be for you... https://t.co/uZMaON9LzE
#robotlearning #Robotics #GenerativeAI #MachineLearning #AI @oxfordrobots
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On my way to #ICRA2024. Looking forward to Japan! Looking forward to seeing old friends and making new ones! And looking forward to presenting some of the work from @a2i_oxford and collaborators in Yokohama with @Jack_T_Collins, @junjungoal and @jannikzuern…
Delighted to be at #ICRA2024. Interested in effective sim-2-real transfer for world models (WeBT7-CC.6)? Or benchmarking for robot assembly (ThAT9-CC.3)? Or predicting lane graphs for autonomous driving (ThBT6-CC.2)? Come and see us to meet, discuss, or just hang-out...
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RIP Daniel Kahneman. And thank you for the inspiration…
behavioralpolicy.princeton.edu
Daniel Kahneman, visionary psychologist whose work laid the foundations of modern Behavioral Science and its applications, has died at 90
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Trajectory optimisation in high dimensional spaces is notoriously hard. What if you could leverage basic experience of what the system can do and let a diffusion model and vanilla sim guide you? Stunning work led by @junjungoal with @ShaohongZhong and @Jack_T_Collins @a2i_oxford
We introduce D-Cubed, a novel trajectory optimisation method using a latent diffusion model trained from a task-agnostic play dataset, including only representative hand motions, to solve dexterous deformable object manipulation tasks! (1/N)
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My group in @oxengsci, @oxfordrobots is looking for talented research students passionate about robot learning. Interested in doing a PhD researching efficient and versatile world models for robotics and beyond? This one may be for you…
If you are excited about world-models in robotics, check out this EPSRC iCASE PhD studentship (fully funded for UK Home students) in @a2i_oxford in collaboration with Siemens: Foundation Models for Industrial Control Applications. *Deadline: 1 March 2024* https://t.co/8oSn89EVVh
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Interested in efficient model-based RL in the real world using visual observations? Then here is one more worth checking out this year: World-Model Distillation, lead by @junjungoal (together with @MarcRigter and @Jack_T_Collins) ... @a2i_oxford #RobotLearning #Robotics
How can we sim-to-real transfer model-based RL with improved sample efficiency? We present TWIST, to achieve efficient sim-to-real transfer of vision-based model-based RL using distillation. 🧵👇
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Inspired work introducing policy-guided #diffusion by @MarcRigter & @junjungoal . The prospect of efficiently imagining entire on-policy trajectories in one go is tantalising. Looking forward to exploring where this can take us... @a2i_oxford @oxfordrobots @oxengsci
Autoregressive next-token prediction is not enough: reliable AI agents are going to require accurate models of the world. I’m excited to share a new approach to world modeling that does not require autoregressive sampling: “World Models via Policy-Guided Trajectory Diffusion”…
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Offsite assembly is important but challenging to automate. Let’s see what we can do …
🧵 Introducing RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. Check out the RAMP benchmark website: https://t.co/8LQ6DYh5p6. 1/5 👇 w/ M. Robson @junjungoal M. Sridharan K. Janik @IngmarPosner @a2i_oxford @oxfordrobots @the_MTC_org
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Choice of information to condition your skill module on heavily influences transfer benefits in Reinforcement Learning. How do we automate this choice across domains? Attend our poster session today at #ICLR2023 and see our paper https://t.co/5oHKLdA8sT.
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