Edge Runtime

Intelligence, processed at the edge.

Xolver Edge transforms raw hardware into a physically intelligent system, processing foundation models, high-frequency kinematics, and safety enforcement locally - with near-zero latency.

Edge_Live_Feed
E1-Core Status: Ready
Hardware: Under DevelopmentLocal dev
Xolver Edge Preview
console_v0.9.4.dev
System: Active

Joint DiagnosticsArm: Under Development

J0112°
Torque4.2 Nm
Load15%
Temp38°C
J02-45°
Torque8.1 Nm
Load22%
Temp41°C
J0388°
Torque12.4 Nm
Load45%
Temp44°C
J04-10°
Torque2.1 Nm
Load8%
Temp36°C
J055°
Torque1.5 Nm
Load5%
Temp35°C
J0645°
Torque3.8 Nm
Load12%
Temp37°C

End-Effector / Haptics

Finger Position

65%

Grip Force

15.0Newton

Tactile State

Stable

Haptic FB

Active

Xolver Edge Telemetry

NPU Load

82%

Latency

11.2 ms

Active Models

  • - TRN-VLM-Edge_v2
  • - KIN-DET-Layer_v0
  • - SAF-ENF-Module
GeofenceActive
Auth: 0x82f...a1 // Data_Throughput: 1.2 GB/sXolver_Edge_Ref: 2026_PROD_BETA

// Engineer's View: Aggregating raw joint currents and tactile sensor inputs into actionable high-level telemetry. Processed entirely on the Xolver Edge NPU.

Deconstructing the Edge Dashboard.

The visualization above makes visible the massive computational work Xolver performs. It isn't just data; it's a window into the system's physical intelligence.

3D Workspace & Sensing

The central viewport displays the 6-DOF arm's digital twin in its environment. Multi-fingered grippers are outfitted with conceptual tactile feedback visuals: pressure grid overlays that visualize the interaction between machine and object in real-time.

Joint Diagnostics (Xolver-Derived)

Xolver calculates Torque and Load (Nm) by processing raw motor signals. This 'Xolver-Derived' data makes invisible mechanics visible, allowing engineers to monitor joint health, thermal performance, and current draw entirely at the edge.

End-Effector Precision & Haptics

Visualized tactile information (including finger position, gripping force (N), and slip detection alerts) enables complex, delicate manipulation tasks. Haptic feedback is represented as force vectors, providing the granular data needed for reliable interaction.

Xolver Edge Telemetry

Proof of edge capability is visible in real-time metrics: NPU utilization, inference latency (ms), and active AI models. Xolver manages high-frequency kinematics and safety enforcement locally, bypassing cloud-dependent bottlenecks.

Environment & Safety

Xolver actively manages environment perception and safety enforcement. Obstacle detection maps, geofence status, and a consistent safety heartbeat ensure the machine stays within bounded autonomy even as the environment changes.

Comprehensive Data Streams

Instead of isolated metrics, Xolver handles multiple parameters across charts, number fields, and graphical overlays—everything an engineer needs to debug, optimize, and trust edge intelligence in high-stakes environments.

Deploy autonomy with boundaries.

Robotics arm and hardware platform currently under active development. Early access available for research partners.