01
Observe
Real-time Perception
Capture live visual feeds and robot sensor data to establish ground truth state.
Frame Rate
60 FPS
Latency
<5ms
- Multi-camera fusion
- Joint state sync
- Depth estimation
BridgeV2W
See the Consequences Before Acting
Action-conditioned visual prediction helps evaluate candidate actions and possible collision risk before execution.
Embodied world model
BridgeV2W: See the Consequences Before Acting
A predictive interface compares candidate actions, visualizes likely outcomes and evaluates risk before the robot commits to execution.
02
Imagine
World Model Forward Pass
Generate multiple future trajectories conditioned on candidate actions using the predictive model.
Candidates
5
Horizon
2.0s
- Trajectory sampling
- Physics simulation
- Scene prediction
03
Evaluate
Risk Assessment
Score each candidate against safety constraints, task objectives, and kinematic limits.
Check Rate
1000/s
Threshold
0.95
- Collision detection
- Joint limits
- Task alignment
04
Execute
Validated Action
Dispatch the highest-scoring trajectory to the robot controller with confidence bounds.
Confidence
98.7%
Dispatch
42ms
- Safe motion
- Real-time monitor
- Fallback ready
Cross-Modal Fusion
Joint embeddings from RGB, depth, proprioception, and language instructions.
Unified representation
Efficient Inference
Latency-optimized forward passes with adaptive compute allocation.
<50ms end-to-end
Generalization
Zero-shot transfer to novel scenes via pretrained visual priors.
No domain adaptation
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.