Jiawei Fu

Robotics PhD @UCSanDiego | Ex-intern @Amazon (FAR), @EPFL_en & MPI-INF | MSc Robotics @ETH_en | BEng ME @Tsinghua_Uni

[Jiawei Fu @JiaweiAcademic]

Impressive dynamic range for tactile sensing! Resolving forces differing by over 1,000× 🚀 within the same frame, without any hardware modifications.

[Arik Slepyan @arikslep]

One of the biggest problems people talk about with tactile sensors is their dynamic range. Most sensors either saturate quickly or aren't sensitive enough to detect light touches.

Why can't we have both?

Introducing HDR tactile sensing with OpenTac.

[Jiawei Fu @JiaweiAcademic]

Excited to share DexPort!
Portable data collection with a real 20-DoF robot hand, recording actual joint commands—not just positions!

[Amber Xie @amberxie_]

Collecting data for dexterous hands is hard. Teleop needs a full setup. Bringing UMI-like devices to 20-dof hands is challenging.

Introducing DexPort 🖐️: a hand-held interface w/ a real robot hand. No embodiment gap, recorded joint state AND actions, easy to use.

[Arik Slepyan @arikslep]

We present OpenTac, an open, full-stack ecosystem for 3D tactile sensing on any robot surface.

Anyone can design and fabricate high-density tactile skins for any part of their robot, then read them out, simulate them, and share data across different sensors.

[Yulin Liu @yulinnliu07]

🤖 For parallel-jaw grippers, high-level plans can already be executed with mature primitives such as grasping. What about dexterous hands?

Introducing GOTT (General Object Trajectory Tracking): object trajectories to dexterous execution with a reusable cross-embodiment contact primitive.

The same primitive supports diverse real-world tasks including sweeping, pouring, whiteboard erasing, and hammering.

With one checkpoint, GOTT reaches 90-96% grasp-and-lift success across multiple objects on three real-world arm-hand platforms.

Check videos 👇

[Zhaoning Wang @Zhaoning_Eric_W]

1/5 🚀 Thrilled to introduce AFUN: a step toward an affordance foundation model for functionality understanding. From a single RGB-D image + a language instruction, AFUN predicts where to interact (a task-conditional mask) and how to interact (a 3D motion curve), and can be directly deployed on a real robot with zero fine-tuning. It generalizes across diverse environments, objects, and tasks.

Code & Weights are public.
Paper: https://t.co/nyZUS2Sr1v
Project page: https://t.co/sU6T7WvQ1Z
Code: https://t.co/cziOC9ZC0F

#Robotics #Affordance #EmbodiedAI #Manipulation #AI

[Danfei Xu @danfei_xu]

I often get this question: Is LLM all you need for robot planning?

I'd go: "obviously not, because you need to consider physical constraints, dynamics, ... ", which then turn into a non-stop rant.

Now I'll just point them to this paper 😎

[apoorv @_apoorvnandan]

all you need to learn about flow matching

[Jiawei Fu @JiaweiAcademic]

@leggedrobotics @junja941 @ETH_en @eth_dmavt @sehoonha Congrats!!!

[AI at Meta @AIatMeta]

E3B is a method for exploring complex environments which vary across episodes. This work set a new SOTA for MiniHack & reward-free exploration on Habitat in October.

Read the paper ➡️ https://t.co/5oxIf1kDAy
Get the code ➡️ https://t.co/omz9GLkkha

[Tom Bielecki @tombielecki]

In the last few weeks:
- https://t.co/FsWd3m8sYg has replaced Google for me.
- Poe has replaced ChatGPT.
- Promptable has replaced OpenAI Playground.
- Elicit has replaced Google Scholar.