Craig Hale

Touchlab is developing thin, biomimetic electronic skin for robots using quantum-tunneling technology to measure pressure, force, direction and incipient slip with sub-millisecond response times. The technology is intended to help robots handle delicate objects and operate more effectively in workplaces, healthcare and homes. Moving from pilots to mass deployment remains difficult because of durability, reliability, packaging and cost constraints. Touchlab plans to combine bespoke integrations with standardized, off-the-shelf components, edge computing and shared autonomy to reduce latency, bandwidth requirements and operator fatigue. The company ultimately aims for tactile sensors to become as widespread in commercial robots as MEMS sensors, while acknowledging that manufacturing and market-adoption challenges remain.

Forward argues that mathematically accurate digital twins can let organizations test network changes before deployment, reducing configuration-related outages, security risks and lengthy change windows. Its platform models the configuration and operational state of network devices across vendors, environments and protocol layers, continuously synchronizing with live infrastructure. The company says Forward Predict provides deterministic path, routing, firewall, segmentation and compliance validation, while Forward AI can iterate toward verified changes autonomously. The approach is positioned as particularly important for autonomous networking, where machine-speed changes could otherwise allow mistakes to propagate at scale.