I designed and performed a transient SPICE simulation of a circuit capable of training a multi-layer perceptron fully in analog, using the Skywater 130nm PDK. It uses crossbar arrays built out of modified 2T1C dram cells to perform (nonlinear) matmuls and opamps as activation functions. Exact gradient updates over multiple layers (taking into account device non-idealities) are computed and applied fully in analog, using the equilibrium propagation algorithm.

Training SOTA imitation learning algorithms, like diffusion policy and ACT, has become easier with recent open source libraries like Huggingface’s LeRobot. But building the pipeline to teleoperate an arbitrary pair of robot arms in real time, recording demonstrations for imitation learning in an intuitive way, requires some expertise to set up. I built a browser app to make this easy: WebXR-based handtracking, connecting via WebRTC to you laptop, which solves the inverse kinematics for arbirary URDFs in WASM in the browser, exporting trajectories containing movements and webcam-vision that are ready for imitation learning. All you need to write is a little python server forwarding joint angles from localhost to your robot.

What always bothered me about smart contracts was that it was incredibly hard to make an actual contract about real-world events. So my friends and I set out to make it easier.