Features
Continuous post-training of agents from real work traces
Signal extraction from outcomes, policies, and expert judgment
Custom reward functions and evaluation metrics
Reinforcement learning to tailor agents to workflows
Memory architecture for compounding learning
Production feedback loop routing outcomes back to training
Evaluation against real constraints and edge cases
Integration with existing tools and data pipelines
Collaborative research and development with Monte team
Custom model fine-tuning and adaptation
Unified omni-bodied AI brain for any robot type
Learns manipulation skills from human video demonstrations
Security/inspection robot navigation in hazardous environments
Mobile manipulation with grasping, handover, and navigation
Autonomous packing with precise dexterous manipulation
API abstraction for high-level task control
General-purpose physical world understanding
Scalable data solution via human video
Unstructured environment operation
Real-world value-focused applications (inspection, packing)
Handles mobile manipulation on wheeled platforms
Autonomous navigation in dangerous settings
Skill execution via API calls for app building
