Hemlock
Hemlock is a YC Fall 2026 (F26) company. Based in San Francisco, CA, USA. Team of 1.
About Hemlock
Summary
Hemlock builds force-annotated demonstration data for robotics AI training. Its Bracelet, a wrist-worn EMG band, reads forearm muscle activity to estimate finger-level grip force during human demonstrations, capturing what video and joint-angle data alone cannot: how hard the hand is pressing. Robotics labs use this to train manipulation policies for tasks such as parts assembly, delicate object handling, and hospital supply workflows, replacing the guesswork that comes from training on force-blind recordings.
Business model
Robotics teams either purchase the Bracelet to collect their own force-annotated data via an API, or commission Hemlock to collect and deliver task-specific demonstration datasets directly.
Go-to-market
Robotics labs and physical AI teams are the buyers; they can self-serve by ordering the Bracelet and uploading recordings through the API, or engage Hemlock to collect demonstrations on their behalf for a specified set of tasks.
From the Y Combinator listing
Force-sensing wristband for training robotic AI
Every serious robotics lab is bottlenecked on the same thing: demonstration data. The field has converged on egocentric video and hand pose as the substrate, and both are now cheap to collect at scale. What neither of them contains is force. A video of someone picking up an egg and a video of someone picking up a rock are nearly identical in pixel space and nearly identical in joint angles. They differ almost entirely in a quantity nobody is recording. Policies trained without it learn geometry and inherit no sense of touch. Hemlock closes that gap at the source. Our device, the Bracelet, is a wrist-worn surface EMG band that reads the electrical activity of the forearm muscles that actually drive the hand, which means it recovers contraction force directly rather than inferring it from motion. This distinction is the whole thesis: force cannot be reconstructed downstream of kinematics, because a hand squeezing hard isometrically and a hand resting can occupy the identical pose. Muscle signal is the only non-invasive channel that separates them.
Facts
- Industrials / Industrials -> Manufacturing and Robotics
- San Francisco, CA, USA
- 1
- Active
- Early
Founders (1)
- Julian Olschwang
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