Spatial Understanding Pretraining
Learning spatial keypoints, object relationships, and motion trajectories from large-scale human operation videos with zero information loss.
Embodied Brain
Open Brain Empowers Hardware Manufacturers
FIVEAGES develops a coordinated embodied model stack connecting ultra-low-shot spatial manipulation, predictive world modeling, and continual reinforcement learning.
Embodied intelligence system
From Spatial Understanding to Predictive and Continual Intelligence
A unified embodied brain stack connecting ultra-low-shot spatial manipulation, predictive world modeling, and continual reinforcement learning.
FAM
FAM Series Manipulation Foundation Model
World’s first ultra-low-shot embodied manipulation large model, achieving lossless transmission and efficient utilization of spatial information through heatmap alignment.
Lossless Heatmaps3–5 Demos AdaptationSub-mm Precision
Lossless Spatial HeatmapsExplore FAM
FIVEAGES
Embodied
Intelligence Core
Open Brain PlatformVision & LiDARSpatial HeatmapsPredictive World ModelReinforcement LearningTrajectory Execution
BridgeV2W
BridgeV2W Embodied World Model
Next-generation embodied world model transforming robot actions into pixel-level motion predictions for foresight and autonomous obstacle avoidance.
Action to Video FutureCollision ForesightPixel-Level Simulation
Predictive ForesightExplore BridgeV2W
Human-in-the-Loop RL
Human-in-the-Loop Reinforcement Learning
Continuous adaptation technology combining demonstrations, error correction interventions, and autonomous optimization to break through generalization bottlenecks.
View Continual Reinforcement Learning Details
Closed-Loop Evolutionary Architecture
Demonstration Pretraining
Few-shot visuomotor policies
Intervention Correction
Real-world dynamic error resilience
Autonomous RL Optimization
Process & outcome dual reward
Preference Alignment
Multimodal human feedback
Embodied manipulation foundation model
FAM Series: World's First Ultra-Low-Shot Embodied Manipulation Large Model
Solving the pain points of insufficient data volume and spatial information loss in traditional VLA models.
Architecture Comparison
"Achieving Results Through Quantity" vs "Achieving More with Less"Data Dependency
Heavily relies on hundreds to thousands of real robot demonstration data
Requires only 3–5 examples; leverages unlabeled videos and synthetic data
Model Architecture
Traditional VLM compresses 3D space into 1D vectors, losing key spatial structure
World’s first "lossless" manipulation model via 2D & 3D heatmap alignment
Task Migration
Adapting to new production lines requires recollecting large data volumes
Data flywheel enables rapid zero/few-shot migration across tasks
Generalization
Bottlenecks for common multi-scenario manipulation tasks
High sample efficiency with robust cross-environment generalization
3D Heatmap Alignment
Directly aligning visual-language representations and robot action policies at the 2D/3D spatial heatmap level.
Ultra-Low-Shot Adaptation
Achieving enterprise-grade task success with only 3–5 real-world demonstrations.
Evaluated on:RLBench ManipulationPeg InsertionDrawer & Valve OpeningVariable LightingBackground InterferenceObstacle Avoidance
Measured under defined conditions
Performance Designed to Be Verifiable
Every product specification and performance claim is linked to configurable test conditions, evidence, evaluation methods and limitations.
88.2%
RLBench Simulation Manipulation Success Rate
97%
Defined Base-Task Success Rate in Complex Real Environments
3–5
Real Demonstrations for Typical Task Adaptation
1%
Data Requirement Compared with Selected Traditional Pipelines
28 DoF
Full-Body Degrees of Freedom
24/7
Continuous Operation with Dual Hot-Swap Batteries
Next-Generation Embodied World Model
World Model BridgeV2W: Foresight & Autonomous Obstacle Avoidance
Given current observation images and action sequences, BridgeV2W outputs future video sequences to enable predictive foresight and collision-free execution.
01
Observe
Multimodal Observation
Capture current RGB-D visual frames and proprioceptive sensor states.
Latency
<5ms
Resolution
Multi-view
- Multi-camera sync
- Spatial alignment
- Scene depth state
02
Imagine
World Model Prediction
Converts candidate robot action sequences into pixel-level motion video futures.
Horizon
2.0s future
Fidelity
Pixel-level
- Action conditioning
- Physics simulation
- Dynamic state mask
03
Evaluate
Autonomous Risk Check
Autonomous obstacle avoidance scoring and kinematic feasibility verification.
Evaluation
Multi-candidate
Safety Rate
99.5%
- Collision foresight
- Singularity check
- Task goal alignment
04
Execute
Foresight-Validated Action
Dispatch the optimal trajectory to physical controllers with confidence guarantees.
Response
<40ms
Dispatch
Deterministic
- Smooth trajectory
- Active compliance
- Autonomous stop
Pixel-Level Action-to-Video
High-fidelity prediction of future visual states across multi-view and cross-platform setups.
Zero representation gap
Autonomous Obstacle Avoidance
Robots possess predictive foresight to evaluate dynamic collision risks before motion.
Foresight decision-making
SOTA Generalization
Outperforms NVIDIA Cosmos and Zhiyuan EVAC under unseen viewpoints and challenging scenes.
State-of-the-Art Benchmark
Continual Learning & Evolutionary Capabilities
Human-in-the-Loop RL: Breakthrough Scene Generalization
Enhancing model usability, long-term stability, and generalization by combining human intervention, error recovery demonstration, and autonomous reinforcement learning.
1
Few Demonstrations
2
Imitation Learning
3
Autonomous Execution
4
Error Intervention
5
Recovery Guidance
6
Reinforcement Learning
7
Continuous Adaptation
Normal Execution
Deviation Detection
Human Intervention
Recovery Learning
Policy Evolution
High Robustness & Long-Term Stability
Low Sample & Parameter Cost
High Alignment with Human Intent
Real-World Dynamic Error Resilience
Deploy in the real world
Deploy Embodied Intelligence in Your Real-World Operations
From task assessment and data collection to model adaptation, robot deployment, system integration and continuous optimization.