Anson Lei

DPhil student @a2i_oxford | Machine Learning, Causality, Robotics

[Jun Yamada @junjungoal]

How can a bimanual robot grasp objects that are initially ungraspable due to environmental constraints?

COMBO-Grasp tackles this challenge by leveraging a constraint policy to stabilise the object while a grasp policy executes non-prehensile manipulation and grasping.

[Ingmar Posner @IngmarPosner]

🚀 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

[Anson Lei @AnsonISL]

Check out the paper for more details!
w/ @IngmarPosner @bschoelkopf
Work done at @a2i_oxford

https://t.co/lQJ2wuXng0

[Anson Lei @AnsonISL]

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.

🧵

[Anson Lei @AnsonISL]

As a result, SPARTAN achieves significantly improved efficiency when adapting to interventions as well as robustness against distractors and changes to irrelevant objects, without compromising on prediction accuracy.

[Anson Lei @AnsonISL]

@Awesome_Boardin Super happy to be back - many fond memories! :)

[Oxford Applied AI Lab @a2i_oxford]

🚀 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

[Ingmar Posner @IngmarPosner]

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…

[Jun Yamada @junjungoal]

Excited to present AMP-LS, our recent work on gradient-based motion planning, accepted to @ieee_ras_icra 2023.

AMP-LS can plan a collision-free trajectory efficiently in novel complex scenes and is trained on only easily generated kinematically valid joint states.

🧵👇

[Bernhard Schölkopf @bschoelkopf]

Our 2012 paper ‘On causal and anticausal learning’ just received a Test of Time Honorable Mention at @icmlconf #ICML2022: https://t.co/gc1FZYSOyP. I am really grateful, and would like to use this occasion for some thoughts on causality and machine learning:

[Anson Lei @AnsonISL]

The end result? With a small amount of observations from an unseen environment, VCD can identify sparse changes in the scene and adapt modularly. This results in significantly improved sample efficiency during adaptation.

3/3

[Anson Lei @AnsonISL]

Super excited to share our new work - a personal first - Variational Causal Dynamics (VCD).
We trained a latent state space model with a causal and modular structure for efficient adaptation.
Work at @a2i_oxford with @bschoelkopf, @IngmarPosner
Paper: https://t.co/1pVshImdgJ
1/3

[Anson Lei @AnsonISL]

VCD learns a causal transition model by performing causal discovery in the latent space. The key is to learn the representation and the transition model **jointly** under sparsity regularisation. In doing so, we get a nice bi-product: disentangled latent representation.

2/3