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.
Joint DiagnosticsArm: Under Development
End-Effector / Haptics
Finger Position
Grip Force
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
// 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.
FAQ
Why does Xolver run model inference entirely at the edge?
Physical systems require deterministic, near-zero latency execution. Running VLA model inference and kinematics calculation locally on edge NPUs ensures the system remains safe and responsive, even during complete cloud connectivity drops.
What does the 'Xolver-Derived' label mean in the diagnostics panel?
It refers to torque and load parameters calculated in real-time by processing raw motor signals. This allows edge systems to diagnose mechanical fatigue and load drifts without requiring additional external torque sensors.
How does the Edge Console integrate with existing HMIs?
The Xolver Edge Console operates as a web-based portal. It can be embedded directly into standard factory HMIs (Human-Machine Interfaces) via standard browser frames, providing operators with clear diagnostic, dry-run, and validation views.