6thSense — tactile capture hardware for dexterous robotics

Tactile capture hardware for the next generation of dexterous robots — gloves that feel, and custom skin molded to robot hands.

Robots have five senses. We are building the sixth.

Why tactile capture hardware

Contact onset and grip evolution are under-captured

Most capture setups miss touch timing, pressure trends, and the subtle grip adjustments that decide contact-rich tasks.

Off-the-shelf stacks produce unusable raw dumps

Standalone sensors and DIY recording scripts fall down on calibration, synchronization, and the reliability real capture demands.

Teams want to capture, not to build a rig

Robot-learning teams want to record their own demonstrations on day one, not spend months sourcing sensors, wiring rigs, and debugging sync.

Manipulation progress is data-constrained

For contact-rich tasks, the next performance gains come from better multimodal demonstrations, not model scale alone.

How the capture stack works

Hardware, sync, calibration, packaging — one stack, four stages. You capture your own demonstrations and get aligned episodes, not raw folders.

  1. Capture — Wearable + egocentric rigs
  2. Sync — One clock, every modality
  3. Calibrate — Drift, fit, and timing checks
  4. Package — Episodes shipped model-ready

Eight aligned modalities, one episode

Tactile & pressure proxies

High-rate contact and pressure-aligned streams with per-channel calibration — where touch matters for the task.

Egocentric video

First-person RGB aligned to what the demonstrator sees — stable exposure for long household runs.

Depth (RGB-D)

Per-frame depth aligned to ego timebase for geometry, reach, and clutter around the hands.

Hand pose

Articulated hand state and grasp phases for contact-rich manipulation — not just 2D boxes in frame.

Motion & IMU dynamics

Linear acceleration, angular rates, and movement cues that characterize inertia, rhythm, and effort during the task.

Wrist & scene cameras

Secondary viewpoints for occlusion recovery, tool use, and context beyond the ego cone (roadmap / program-dependent).

Labels & dense commentary

Task and subtask boundaries, contact phases, QC flags — plus optional timestamped, frame-aligned text narration paired to video for richer training supervision.

Success / failure outcomes

Binary or graded success, failure modes, and segment-level tags for imitation and evaluation.

Representative task families

How we earn trust

Calibration boundaries, stated

We document where each signal is reliable, how drift is handled, and what should never be treated as ground-truth force.

Semantics you can train on

Pressure proxies, contact timing, and failure flags are defined so policy teams know exactly what each dimension means.

Model-ready packaging

Episodes land in the formats your trainers expect, with QC metrics and assumptions — not a dump of raw sensor folders.