@EkaRobotics has raised a $150M Series A. Generality v/s Performance is the fundamental tension at the frontier of robotics. Generality without performance is just a prototype. Performance without generality is just a single-purpose tool. Customers shouldn’t have to choose. Eka breaks the tradeoff. Powered by our Vision-Force-Action (VFA) model, we deliver the performance and versatility customers need to put robots to work every day. Eka is built on three convictions: ⚡️Force is the language of manipulation. 🚀 Reinforcement learning is how dexterous manipulation scales. 🎂 System 1—fast, reliable physical execution—is the bottleneck to solve. With this funding, we will scale our model and accelerate customer deployments. Grateful to be building alongside a dream team and investors who share our ambition.

Every robot already carries contact sensors: its own joints. When a robot touches something, its joints drift slightly off their commands, and the encoders pick it up. Legged robots and humanoids already leverage this signal to walk blind - but is it enough for blind precise assembly? We find that it is! Our policies can insert pegs, mesh gears, and thread nuts using only the robot's joint positions - no camera, no tactile, and no object poses. And the peg policy works zero-shot in the real world! 🧵 w/ my amazing co-first author @younghyo_park, and advised by @kushalk_ and @pulkitology.

(1/9) Same rewards. Less domain randomization. Higher jumps. Stable landings. Just by improving the actuator model. Introducing Contact-UAN: a residual world model for humanoids, learned from proprioception. No torque sensing or motion capture. Project website: contact-uan.csail.mit.edu.

(1/6) From @GoogleDeepmind’s AlphaGo to AlphaZero, we saw machines find moves that reached beyond what we’d imagined for them. No one taught them those strategies. They emerged from reinforcement learning: playing, failing, and trying again under a single binary task reward for success or failure. Inspired by them, we couldn’t help but wonder: what might a robot discover if we gave it that same freedom to learn? That question led us to AlphaNIST. Starting tabula rasa, with that same binary reward plus only a simple control cost, a single policy solves all 20 @NIST assembly tasks at a 98.2% success rate in simulation. More surprising still, AlphaNIST can complete 10 different tasks in a row, receiving that reward only once, after all 10 are done. Just as AlphaGo’s strategies surprised its creators, AlphaNIST discovered dexterous strategies we couldn’t have come up with ourselves. Paper: arxiv.org/abs/2610.13354 Website: alpha-nist.github.io

What to remember in the past to act—spatial, temporal or motion details? We often assume what’s useful, but that’s hard to know in advance. Meet IMPRINT 🧠: retain rich memory first, then learn to use it for control. Compact latents keep inference fast as history grows. 🧵 1/10

Taking humanoid soccer out of the lab: no instrumentation, no controlled environment. Real fields, around people. Trained in simulation. Deployed in reality. No post-training. Just pre-training. Dribbling. Passing. Turning. Scoring goals. Work led by Avi, Khai, @nolan_fey along with @martinpeticco @JohnMarangola, and Venki.

In-context continual learning requires models to accumulate experience and reuse it later in the same sequence. But an RNN compresses an ever-growing history into a fixed-size state, where each token gets a single write into memory. We study dynamic compression: letting the model revisit the past and reorganize its state as it discovers what needs to be reused.

Continual learning running on a phone. Not inference. Not a fine-tune you kick off with a dataset. A small language model (SLM) that keeps learning from its own experience while it's being used, with every gradient step on the handset. How it works (lin826.github.io/SLM-Online-…):

Excited to share SPD: simulation pre-training for dexterity. We pre-trained a policy in simulation and fine-tuned with less than 2 hours of real data (with @sarthakkamat)

You already know prompting can change what an LLM does. Turns out it can change what a robot does too — we made a robot learn a task it kept failing just by rewriting the prompt. No retraining. No new data. Better prompt in, better robot out. 🧵 1/8