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  • WAVE 5 // DEVELOPMENT
  • WORLD MODEL
  • DYNAMICS-AWARE

TITAN

WORLD-MODEL VLA

GigaBrain-0.5M architecture: pretrain world model predicting environment dynamics, condition VLA policy on world model predictions, collect rollouts for improvement, human-in-the-loop correction. World model provides dynamics priors that improve sample efficiency and long-horizon reasoning.

MODULE STATUS: DEVELOPMENT

PYGMALION + ATOMOS

WORLD

DIVISION
ANIMA
WAVE
W5
DOMAIN
MANIPULATION
WAVE 5 // ANIMA SUITE
MANIPULATION — WORLD-MODEL VLA
TITAN // W5 // 075/079
01THE CHALLENGE

POLICIES WITHOUT PHYSICS ARE BLIND

Standard VLA policies map observations directly to actions without any model of how the world works. They can't predict what happens when they push an object, can't anticipate physics, and must learn every cause-effect relationship from scratch through expensive trial and error.

This lack of world understanding means poor sample efficiency — thousands of demonstrations for tasks that a child learns in minutes. Long-horizon tasks compound the problem: without dynamics prediction, each step accumulates uncertainty. TITAN gives VLA policies a world model to reason with.

02THE SOLUTION

WHAT TITAN DELIVERS

TITAN conditions VLA policies on a pretrained world model that predicts environment dynamics. The world model provides physics priors, the VLA policy generates actions conditioned on predicted futures, rollouts refine both, and human correction closes the loop.

PIPELINE

  1. 01World model pretraining — learns environment dynamics from observation data, predicting future states from current state and action
  2. 02VLA conditioning — policy network receives world model predictions as additional context, grounding actions in predicted physics
  3. 03Rollout collection — autonomous and guided rollouts generate improvement data for both world model and policy
  4. 04Human-in-the-loop correction — expert corrections on predicted trajectories refine world model accuracy and policy behavior

CAPABILITIES

  • WORLD MODEL CONDITIONINGVLA policy conditioned on predicted environment dynamics from PYGMALION→ Actions grounded in physics predictions, not blind pattern matching
  • SAMPLE-EFFICIENT LEARNINGWorld model provides dynamics priors that reduce demonstration requirements→ Learn complex manipulation from fewer demonstrations than end-to-end approaches
  • LONG-HORIZON REASONINGMulti-step planning through predicted future states via ATOMOS temporal modeling→ Chain actions over extended time horizons with physics-aware prediction
  • HUMAN CORRECTION LOOPExpert feedback on predicted trajectories refines both world model and policy→ Continuous improvement from human expertise without full re-training
03ENGINEERING

WHY THIS IS HARD

Conditioning a VLA on a world model creates compounding complexity:

  1. 01World model must be accurate enough to be useful but not so complex that it becomes the bottleneck
  2. 02Conditioning interface: VLA must consume world model predictions without becoming dependent on their accuracy
  3. 03Prediction horizon trade-off: longer predictions enable better planning but accumulate error exponentially
  4. 04Rollout distribution shift: self-generated data drifts from human demonstrations, requiring careful mixing
  5. 05Human correction must be efficient — experts correct trajectory predictions, not raw model weights

TITAN handles this with a modular architecture: the world model (PYGMALION) and temporal reasoning (ATOMOS) are separate components with clean interfaces, allowing each to improve independently while the conditioning mechanism remains stable.

04BENCHMARKS

SYSTEM PERFORMANCE

Measured across the world-model conditioned pipeline:

SYSTEM PERFORMANCE
METRICVALUE
World Model ConditioningPolicy grounded in dynamics predictions
Sample EfficiencyImproved via dynamics priors — fewer demos needed
Long-Horizon ReasoningMulti-step planning through predicted futures
Physics UnderstandingLearned dynamics model for manipulation
05BUILD STATUS

WHAT'S BUILT TODAY

2/6 COMPONENTS COMPLETE
WHAT'S BUILT TODAY
COMPONENTSTATUSNOTES
World Model PretrainingCOMPLETEGigaBrain-0.5M dynamics model trained on manipulation data
VLA ConditioningIN PROGRESSPolicy-world model interface being refined
Rollout CollectionIN PROGRESSAutonomous rollout pipeline with quality filtering
Human Correction LoopPLANNEDExpert correction interface design complete
Core modelsCOMPLETEWorld model and base VLA production-ready
API layerIN PROGRESSPublic API, pending dataset infrastructure
06APPLICATIONS

WHERE TITAN DEPLOYS

  • APP_01

    LONG-HORIZON PLANNING

    Multi-step manipulation tasks that require predicting consequences of actions — assembly, cooking, tool use — where physics understanding reduces failure.

  • APP_02

    SAMPLE-EFFICIENT LEARNING

    New manipulation skills from minimal demonstrations — the world model fills in physics knowledge that would otherwise require thousands of examples.

  • APP_03

    PHYSICS-AWARE MANIPULATION

    Tasks involving dynamic interactions — pouring, pushing, stacking — where predicting object dynamics is critical for successful execution.

07TECHNOLOGY

UNDER THE HOOD

FOUNDATION: GIGABRAIN-0.5M ARCHITECTURE

  • World model pretrained on manipulation observation data
  • Dynamics prediction: current state + action → predicted next state
  • VLA policy conditioned on world model latent representations
  • Iterative refinement through rollout collection and human correction

TITAN IMPLEMENTATION

  • World model: PYGMALION video generation backbone adapted for dynamics prediction
  • Temporal reasoning: ATOMOS sequence modeling for multi-step future state prediction
  • Conditioning interface: world model latents injected into VLA policy via cross-attention
  • Rollout pipeline: automated collection with quality scoring and distribution tracking

ANIMA MODULE INTEGRATION

  • PYGMALION provides the world model backbone for dynamics prediction
  • ATOMOS provides temporal sequence modeling for long-horizon reasoning
  • Combined as world-model-conditioned VLA for manipulation planning

ARCHITECTURE SPECS

  • World model: 0.5M parameter dynamics predictor
  • Conditioning: cross-attention latent injection
  • Rollout: automated quality-filtered collection
  • Correction: human trajectory annotation interface
08PAPERS

RESEARCH BASIS

  1. [01]GigaBrain-inspired world-model conditioned VLA — dynamics-aware policy learning with human-in-the-loop refinement