Next-gen embodied intelligence robot brain OS

SNN-architecture
brain-inspired robot intelligence system

A brain-inspired core that is fast to react, deep to reason, able to predict, remember, learn, and show warmth. It breaks the structural limits of no long-term memory, high power, and no continuous evolution—delivering a 24×7 online, low-latency, self-evolving intelligence base for real robots.

Real-time efficiency board
< 100ms Fast-path reflex latency
60%+ lower vs. Transformer inference energy
7×24 Continuous online autonomy

Six core technology pillars

On a brain-inspired base, close the full stack from perception and cognition to memory, emotion, and continuous evolution—so robots truly have a “brain.”

SNN brain-inspired sensing

Low-power event-driven sensing with spiking nets, <100ms reflex—built for real-time obstacle avoidance, touch feedback, and dynamic balance.

Mamba-2 long-sequence modeling

Linear-complexity state-space models for million-token sequences—multi-day/week context understanding and behavior planning.

World-model prediction

Predict action outcomes and reason causally with an internal world model—“foresee” before acting to cut trial cost and raise success.

Six-layer full memory

Perception, episodic, semantic, procedural, affective, and safety memory—persistent multi-day, multi-scene context and personalization.

Auto-learning engine

Continuous online self-training after deploy—optimize policy and memory from real interaction for a “smarter over time” loop.

Affective state model

A brain-like affective state machine gauges emotion, stress, and trust—tuning style, priority, and risk sensitivity for warmer interaction.

Fast/slow dual-path brain architecture

Mirrors human fast (reflex) and slow (deliberate) thinking—balancing high responsiveness with deep decisions.

Fast path · brain-like reflex

System 1 — intuition & reflex

Latency < 100ms

Event-driven SNN compute activates neurons only on sensory events, idling at ultra-low power—reflex responses without a full forward pass.

  • Real-time avoidance, fall detection, and e-stop
  • Instant pose adjust from touch feedback
  • Low-power idle; event-triggered wake
Slow path · deep reasoning

System 2 — reason & plan

Complex planning & causal reasoning

Joint Mamba-2 and world-model reasoning for multi-day/week planning. Multi-step outcome prediction via internal simulation before acting.

  • Multi-day/week task planning and context
  • Action outcome prediction & causal reasoning
  • Multi-objective trade-offs and optimal policy

Six-layer memory pyramid

From millisecond sensing to long-horizon semantics—six layers build self and world knowledge for lasting, coherent, personal interaction.

1

Perception memory

Timescale: milliseconds · Form: sparse vectors

Stores spike-coded sensor features—touch texture, visual contours, audio spectra—as a real-time buffer driving fast-path reflexes.

2

Episodic memory

Timescale: minutes to hours · Form: temporal sequence vectors

Stores full interaction episodes at place/time—e.g. “catch-ball in the living room last Wednesday”—for reuse and fast decisions.

3

Semantic memory

Timescale: long-term stable · Form: structured knowledge graph

Abstracts world and domain knowledge into a queryable graph—“balls bounce,” “handles open doors”—for deeper understanding.

4

Procedural memory

Timescale: long-term stable · Form: skill params & policy nets

Stores learned skills and policies—cleaning paths, grasp poses, patrol routes—for reuse and composition.

5

Affective memory

Timescale: interaction accumulative · Form: affective state sequences

Records affective events—“happy when praised,” “frustrated on failure”—to tune behavior and build trust.

6

Safety memory

Timescale: permanent · Form: rule constraints & boundary matrix

Highest-priority safety bounds—“never touch heat,” “fall-height limits”—as the final filter for zero safety incidents.

Memory write & update

New memories sparse-encode into layers; episodes compress to semantics; procedural memory fine-tunes policy nets continuously.

Memory retrieve & associate

Parallel retrieval across layers by context vectors; similarity ranks the most relevant fragments for cross-layer association.

Memory decay & consolidate

Low-access memories decay to free space; high-frequency cores consolidate via replay for efficient storage.

Embodied scenes & decision flow

Using a home service robot, see the full decision chain from voice wake to task done—brain-inspired core in a real scene.

T+0s

Voice wake & intent

User: “Please clean the living room, and put the cup on the coffee table into the kitchen.”
Slow path (Mamba-2) parses long speech for dual intents: clean + carry. World model predicts living-room state; memory retrieves layout and table location.
T+2s

Task decompose & path plan

Robot: “Sure—I’ll clean the living room and take the cup to the kitchen.”
Slow path splits the compound task; procedural memory calls cleaning path skill; world model orders: move fragile items first, then clean to avoid collisions.
T+15s

Navigate & dynamic avoid

While moving, a child suddenly runs across the living room.
Fast path (SNN) detects the dynamic obstacle in <100ms and e-stops. Slow path rates risk; world model updates path and plans a detour. Safety memory keeps the child absolute priority.
T+45s

Grasp & fine manipulation

At the table, the robot identifies and gently grasps a ceramic cup.
Perception memory encodes touch (pressure, temp, slip); procedural memory calls fragile-grasp params; affective memory senses user presence and tunes speed/force for zero damage.
T+90s

Complete & update memory

Robot: “Done. Living room cleaned; cup placed by the kitchen sink.”
Episodic memory stores the episode; semantic updates living-room cleanliness; affective records positive feedback; procedural fine-tunes the cleaning path—continuous evolution.

Embodied decision-flow visualization

Click a scene to watch fast-path reflex vs. slow-path reasoning switch live, and how memory layers cooperate.

Dynamic avoidance

Fast path active < 100ms

SNN sensing detects a dynamic obstacle inside the safety bound; spikes reach motion control in 80ms for e-stop and pose stabilize. Slow path starts; world model evaluates detour and updates global path. Safety memory locks “pedestrian absolute priority” under zero-collision.

Commercial delivery & SDK stack

For robot OEMs, embodied-AI research teams, and industry solution providers—full-stack delivery from algorithm core to hardware adaption.

KortexFlow Core

Brain-inspired intelligence SDK

C++/Python SDK with SNN sensing, Mamba-2 reasoning, world-model prediction, six-layer memory, and affective state machine—plug into ROS 2/DDS.

KortexFlow Sim

High-fidelity sim training

Physics-engine digital twins with million-scale parallel training. 20+ home/industrial/medical templates plus no-code policy training and distillation.

KortexFlow Edge

Edge deploy & optimization kit

Quantize/compile for Jetson, RK3588, Horizon, etc.—INT8/FP16 mixed precision, 80%+ size cut with <2% accuracy loss.

KortexFlow Cloud

Cloud collab & evolution platform

Fleet remote monitor, data return, federated model update, and A/B tests—with multi-tenant isolation and private deploy for compliance.

KortexFlow DevKit

Dev kit & reference design

Hardware kit with sensor board, compute, sample mechanics, and wiring—plus 50+ samples and docs to prototype in two weeks.

KortexFlow OEM

Industry custom & OEM

For eldercare, industrial inspection, and logistics—scenario algorithms, labeling, safety certification, and co-brand OEM.