One day we will have the equivalent of the gpu compute Azure has in an iPhone and this regulation will seem comical to our children.

New work on FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning: Paper: arxiv.org/abs/2606.12406 Web: jasonjzliu.com/factr2/ FACTR 2 shows that learned force signals can both enable force-feedback teleoperation on low-cost manipulators and improve behavior cloning (BC) policies for contact-rich tasks. It consists of two components: 1. Neural External Torque Estimation (NEXT): A lightweight model that infers external joint torques without dedicated force sensors. 2. Force-Informed Re-Sampling Training (FIRST): A training strategy that uses the learned force signal to identify and upsample task-critical moments. The key insight is simple: policy failures rarely occur in free space, they occur during brief pre-contact alignment and contact-rich interactions, where precise corrections matter most. Together, NEXT and FIRST bring force-aware teleoperation and robust long-horizon contact-rich policy learning to off-the-shelf robot arms, without requiring additional sensing hardware. See a more detailed thread by @JasonJZLiu.

First full draft of my 🆕 lecture on (vanilla) policy gradients done 😅 TODO: Variance reduction methods (temporal structure and baselines).