Force is arguably the most overlooked ingredient in modern robot learning.
Introducing FACTR 2: it turns any commodity robot into a force-aware system with no force sensors required.
Train a tiny force network in <1min with <10mins of data and drop it into any existing teleop pipelines:
✅ Free force sensing for both the robot and the operator arm
✅ Makes demos higher-quality → fewer of them needed.
✅ A new force-aware learning algorithm (FIRST) uses those recovered forces to figure out which parts of a demo actually matter, making learning data-efficient.
✅ Strong performance on complex tasks with fewer demos and even no pretraining!
More details below.
Video
Jason Liu (@JasonJZLiu)
💥Introducing FACTR 2, learning external force sensing on commodity robot arms without needing dedicated sensors.
We show that learned force signals enable force-feedback teleop on low-cost arms and improve BC policies.
FACTR 2 consists of:
1. Neural External Torque (NEXT): learns external forces without needing dedicated force sensors.
2. Force-Informed Re-Sampling Training (FIRST): uses the learned force signal to identify task-critical regions and upsample them during training.
w/ @StevenOh_ @tonytao
🧵(1/N)
Video
— https://nitter.net/JasonJZLiu/status/2065067670819500422#m