Iván López Broceño - MSc Robotics Engineer @TUDelft - Software Robotics Engineering Bachelor @URJC
What I cannot create, I do not understand - R. Feynman
Boston/New England robotics folks:
I have a contact starting up a food service robotics effort and is looking to fill an AI founding engineer / director role.
This is a pre-seed opportunity, but a customer is lined up.
If interested, please DM me and I can share more info.
we're hiring at Physical Intelligence to make robots learn in the real world
if you're a hands-on hardware operator or a product thinker who wants to work on robots my DMs are open
https://t.co/T6LT7SOiLK
Introducing NEO’s 25 Degrees of Freedom, tendon-driven hands — nearing or surpassing human-level dexterity, strength, speed, and reliability.
For seventy years, robotics worked around the hand problem. The humanoid bet is the reverse: it lives or dies at the fingertips.
It's amazing what you can do without machine learning. I've spent hours watching my simple implementation of the Dynamic Window Algorithm, basically a model-predictive control method. Runs live in the browser at this link: https://t.co/jxlLSQ1lVv
Introducing InfiniteDiffusion, my independent paper accepted to #SIGGRAPH2026!
I have one RTX 3090 Ti. No funding, advisors, or team. By day I'm a new grad SWE at Walmart.
The paper has two main contributions:
- InfiniteDiffusion: a new approach to infinite generation with diffusion models.
- Terrain Diffusion: the world’s first learned procedural terrain generator.
Here’s why this matters, and how they are connected. 🧵
𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀.
Most robot learning systems assume failure is the end of learning.
In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels.
The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop:
👉 pretrain on human videos
👉 deploy robot policy
👉 observe failures
👉 reinterpret failures using human priors
👉 improve autonomously
We evaluate this across 7 real-world manipulation tasks, showing:
📈 40% → 81% success rate
🏆 Strong improvements over π0.6 RECAP and RISE
✔️ Zero human intervention during post-deployment improvement
🧬 Generalizes across robot embodiments and policy backbones
A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy).
Overall, the results suggest a shift in how we think about robot learning:
Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment.
📄 Paper: https://t.co/lYJxq9jJQL
🌐 Project: https://t.co/fALv2aC3Ys
I am grateful for working with the fantastic leads @hanzhic678 and @Anran_zh, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to @StefanLeuteneg1 for co-advising this project with me.
@ETH@TU_Muenchen@Microsoft
Check out Hanzhi's 🧵 for more details https://t.co/fsoPTUM6IB
Apertus is now also present on X!
The @ETH_en, @EPFL_en and @cscsch co-initiative is one of the only ones in Europe that seem to have a real shot at building a lab culture similar to one from a proper frontier lab.
There isn’t too much pressure on project-based results, instead there is a vision on compounding the learnings inside the team, iteratively producing better models and also sharing with the academic community
Rodrigo Pérez-Rodríguez y yo hemos creado una web para hacer accesible nuestro libro de Arquitecturas Software para Robots que hemos creado en el @IntellRobotLabs de la @urjc
Incluye el libro y las traspas que hemos usado durante el curso.
https://t.co/Sbxxt54vx3
IK alone can take you pretty far.
I put together a really cute demo to demonstrate this. Watch this Panda robot CNC engrave the MuJoCo logo into a curved dome using mink.
Now that #IROS26 results are out, I would like to share a few thoughts of the peer-review process as AE.
1. If you submit, you must review at least 2 papers! Even if not entirely on your topic, you can still as a researcher look for sources and learn whether the work is novel...
Today, we bring SAC to RSL-RL, one of the most widely used RL frameworks in massively parallel robot learning, developed at RSL @leggedrobotics.
We try to understand the long-standing performance gap between SAC and PPO, and crystallize important factors
https://t.co/QbmBfpUPo3
@WenCabrel_@pesarlin there are ways to anonymize code repositories before publicly release it after acceptance, but that's not the issue here. The paper is now accepted with a project page and blog post, but still no code.