Sensing layer
A conformable, low-profile film designed to wrap real geometry — fingers, palms, forearms and end-effectors — without compromising fit or feel.
SoftShell combines conformable tactile sensing, low-noise signal capture, and integrated fast processing into a single deployable infrastructure layer. The result is a tactile system that scales from human demonstration to real robotic deployment without changing hardware.
Most flexible sensors solve one slice of the problem — coverage, fidelity, processing or deployment. SoftShell is engineered as a complete stack so the same sensing layer remains useful all the way from demonstration to deployment.
SS-MESH / CONFORMABLE FILM
v0.2 SAMPLE
The SoftShell stack is engineered around four functional layers — each one solving a specific failure mode of conventional flexible tactile sensing.
A conformable, low-profile film designed to wrap real geometry — fingers, palms, forearms and end-effectors — without compromising fit or feel.
A low-noise capture architecture targeting the fidelity required for precision manipulation, instead of trading signal quality for conformability.
Integrated processing at the sensor level so signals are conditioned and encoded close to the source — reducing host-side bottlenecks.
High-throughput transmission engineered for the rates real manipulation requires, so the tactile layer never becomes the bottleneck of the platform.
System architecture diagram · v2
glove ↔ skin · single sensing format
Signal fidelity
Conventional flexible sensors typically trade fidelity for conformability. SoftShell is built around a signal architecture designed to preserve precision-task fidelity even as the sensing layer wraps real geometry.
Coverage
Manipulation rarely happens at fingertips alone. SoftShell is designed for full-surface coverage across hands, palms and forearms — replacing point-contact instrumentation with a continuous tactile surface.
Most embodied AI pipelines hit a wall when human demonstration data has to be remapped onto robot hardware. SoftShell removes that translation step entirely. The sensor is the same, the data format is the same, and the embodiment is the same — across glove and skin.
Single sensing format
Identical signal layout from glove to skin — no per-platform calibration tax.
No retargeting
Demonstration data lands in deployment without remapping or interpolation.
Consistent embodiment
The data the model trained on is the data the robot perceives in production.
Unified evaluation
One sensing substrate makes A/B and longitudinal evaluation tractable.
Embodiment continuity
Glove
Worn on human hand
Demonstration capture
Pipeline
Same sensor format
Sensor → encode → transmit
Skin
Wrapped on robot end-effector
Production deployment
SoftShell is designed to support the workloads that current robotic systems struggle to perform without rich, high-fidelity contact feedback.
Multi-finger grasping, in-hand reorientation and contact-rich tasks that require continuous tactile feedback rather than discrete fingertip pads.
Capturing human demonstration with the same sensing format used at deployment, enabling cleaner imitation pipelines with no retargeting layer.
A standardized tactile substrate that makes contact-rich training data portable across platforms, models and research groups.
High-fidelity, low-noise contact signals at the prosthetic surface — designed to feed closed-loop feedback systems and BCI-adjacent stacks.
Bringing existing humanoid, industrial and research platforms online with tactile sensing without redesigning the hardware they ship today.
A consistent substrate for measuring tactile capability and progress — across labs, platforms and product generations.
We share architecture details, signal characterization and integration paths under NDA with serious design partners and frontier labs.