Self-Evolving Robot Intelligence

Self-Evolving RobotsBuilt for Real Work

Ticos gives robots memory, skills, feedback loops, and operational context so they improve through every real interaction.

Learns from real service replays

Turns feedback into safer behavior

Four core systems for self-evolving robots

These are not isolated features. They form one self-evolving chain: connect the robot body, capture realtime interaction, cultivate behavior continuously, and close the operational loop.

Robot cloud platform

Connect the body so evolution has a runtime

Bring validated robot bodies into one cloud platform so every self-evolving capability has stable device status, version control, training release, and fleet operations underneath it.

Support humanoid, quadruped, wheeled, and desktop robot bodies

Unify versions, policies, status, and fleet management

Train in the cloud and run in real time at the edge

Robot body access panel

Unified access / unified operations

Fourier GR-1

Unitree G1

Realman

Unitree Go2

Stardust realtime interaction engine

Give evolution a realtime interaction loop

Stardust captures the live signals self-evolving robots need: speaker changes, interruptions, context shifts, local perception, and motion feedback in noisy real-world environments.

Track the active speaker to reduce wrong handoffs in multi-person scenes

Support interruption, continuation, and stable multi-turn context

Coordinate local voice, vision, and motion control at the edge

Stardust

Realtime path

Speaker recognition, local speech processing, edge vision, context continuity, and motion control run in one low-latency path.

Speaker tracking

Local speech processing

Low-latency response

Edge vision coordination

Cultivation Engine

Turn feedback into self-evolving behavior

A dedicated cultivation flow turns one-off tuning into stable long-term learning across personality, memory, skills, and etiquette.

Use dialogue and demonstrations to shape personality and expression.

Let preferences, relationships, and context accumulate into long-term memory.

Identify capability gaps and strengthen skills through the next operating cycle.

Cultivation engine

Cultivation, not one-time configuration

You

Make her the brand reception robot. Remember important customer preferences. During reception, greet first, confirm intent, then coordinate the meeting workflow.

Cultivation engine

Recorded. Persona, preference memory, reception etiquette, and role skills will settle into this robot's stable service behavior.

PersonaCharacterMemorySkillsEtiquetteRole flow

Production lock

Freeze stable versions to avoid continuous mind drift.

Rollback and fill gaps

Rollback deviations and add missing skills instead of restarting everything.

Closed-loop operations

Close the loop from every interaction to the next version

Self-evolving behavior only works when live problems become diagnosed fixes, published changes, and verified improvements in the next real role.

Monitor device status, event streams, and abnormal changes in real time

Use replays and diagnostics to locate expression, process, or tool issues

Turn correction, release, and review into a repeatable operating rhythm

Operations console

Monitor / replay / diagnose

Online status

Observe robot and task status in real time

Replay diagnostics

Review real service sessions and locate issues

Quality metrics

Track completion, quality, and exception rates

Republish

Bring fixes back online for validation quickly

Platform Capabilities

Core capabilities for self-evolving robots

From memory and perception to skills, safety, and multi-model routing, Ticos organizes the capabilities robots need to keep improving after deployment.

Personalized Character

Stardust 5.0 provides flexible personality APIs, letting developers easily configure emotional traits and language styles, making each robot a unique digital being.

Emotion TraitsLanguage StyleBehavior Patterns

Atomic-level Perception Sync

Ensures precise synchronization of language output with body movements and native expressions for human-level interaction.

Full Audio-Visual Perception

Fuses visual, audio, and tactile perception inputs to build complete spatial memory for the brain.

Millisecond Neural Reflex

With edge computing, robots process intent and respond instantly, like human neural reflexes.

Native Professional Skill Ecosystem

Natively supports skill mounting and dynamic orchestration, with autonomous API calls and training manual retrieval.

Seamless Top-tier Model Access

Native integration with major domestic and international LLMs, providing powerful backing.

Remember people and context

Identify people, accumulate preferences, and retain interaction history so every service moment can improve the next one.

Connect tools and business systems

Link model responses, tool calls, navigation, and enterprise systems into flows that deliver outcomes.

Keep training and optimizing

Use replays, feedback, monitoring, and operations loops to evolve after launch instead of staying fixed.

Multi-model Architecture

Intelligent Routing & Selection

Smart model selection based on task type, cost, and performance for seamless multi-model switching.

Scene-optimized Deep Tuning

Customized model strategies for different robot scenarios, maximizing effectiveness in specific environments.

Innovative Architecture

Unique adaptation layer and orchestration engine supporting parallel multi-model operation with unified API.

Business Flexibility & Security

Avoid vendor lock-in, protect enterprise data privacy with robust security mechanisms.

Production Scenarios

Put self-evolving intelligence into real jobs

Not just conversation, but memory, skill growth, and scene-aware improvement working inside real service flows.

Guided Tours

Museums · Showrooms · Experience Centers

Interest memoryRoute guidanceAdaptive explanation

Go beyond exhibit narration by remembering visitor interests, adapting tour depth, and guiding people through personalized routes.

Results shaped by continuous cultivation in live roles

Learn more

Business Reception

Front Desk · Meeting Spaces · Brand Venues

Guest identificationWorkflow orchestrationService etiquette

Recognize visitors, recall prior interactions, and trigger meeting-room booking, host notifications, and reception workflows through Skills.

Results shaped by continuous cultivation in live roles

Learn more

Care and Companionship

Nursing Homes · Rehab Centers · Home Care

Long-term companionshipDaily remindersWarm interaction

Remember habits and communication preferences to deliver more natural reminders, companionship, and day-to-day assistance.

Results shaped by continuous cultivation in live roles

Learn more

Compatible with diverse robots and edge hardware

From humanoid and quadruped robots to wheeled and desktop devices, bring compute boards, perception modules, and control pipelines into one platform.

Supported Robot Types

Ticos uses modular architecture to support all robot platforms. Import via URDF/MJCF to quickly adapt your hardware.

Humanoid Robot

Bipedal walking humanoid robots with highly human-like motion and interaction capabilities

Typical models:

G1, GR-1, GR-2, H1

Quadruped Robot

Stable and agile four-legged platforms for complex terrain exploration and inspection

Typical models:

Unitree Go2, Go1, A1, B2

Wheeled Robot

Efficient wheeled mobile robots, widely used in logistics and service scenarios

Typical models:

TurtleBot, Tracer, Bunsen

Desktop Robot

Compact desktop devices for human-machine interaction and office assistance

Typical models:

Desktop robots, Interactive terminals

Platform Compatibility

Can't find your robot?

NVIDIA Jetson

Raspberry Pi

ESP32

Android Board

RTK

Import custom URDF/MJCF model

Ticos uses modular architecture to support all robot platforms. Import via URDF/MJCF to quickly adapt your hardware.

Import custom URDF/MJCF model

How does a robot become self-evolving?

Connect runtime, teach the role, then turn live feedback into the next safer and more useful version.

Self-evolving loop

Teach, observe, improve

Use chat, demonstrations, replay review, configuration changes, and diagnostics to shape the robot through continuous operational feedback.

Cultivate the robot mind

Use ongoing dialogue to shape identity, expression, and context

You

"You are the reception robot for the Ticos showroom. Remember that Director Wang cares about multi-robot collaboration. Speak professionally and warmly: greet first, confirm intent, then continue from her previous interests."

Ticos

Understood. I will receive visitors with a stable professional style and remember important customer interests and context so each conversation feels continuous.

Result: persona, tone, etiquette, preference memory, and context understanding become part of the robot mind.

Deployment and purchasing questions

Frequently Asked Questions

Core questions about cultivating robots for real work.

What is the difference between Skills and a knowledge base?

A knowledge base answers "what the robot knows." Skills answer "what the robot can do." You need both to make a robot accurate and useful in real workflows.

Why emphasize self-evolution instead of one-time configuration?

Real roles change after launch. Robots need to learn from interaction, replay, feedback, and operations data so memory, skills, and service behavior improve over time.

How does the robot become more helpful after launch?

Ticos uses monitoring, replay, diagnostics, and continuous optimization to turn real-world feedback into better expression, workflow execution, and service behavior.

Who is Ticos for?

Robot makers, system integrators, and end customers can all use Ticos to turn hardware into role-ready robots with memory, business understanding, and execution.

Build a self-evolving robot role

Tell us about the role, service environment, and interaction requirements, and we will help map the robot body, evolution loop, platform capabilities, and rollout plan.

Define role and setting

Clarify users, service environment, and task boundaries

Plan the rollout path

Align robot body, cultivation method, platform stack, and delivery rhythm