Features
Physical world foundation model for robot control
Multi-task learning across manipulation, locomotion, and navigation
Simulation-to-real transfer with domain randomization
Hardware-agnostic deployment via unified API
Real-time perception and motion planning
Generative trajectory prediction
Zero-shot transfer to new environments
Safety constraints with conformal prediction
Model fine-tuning on custom tasks using few demonstrations
Integration with ROS 2 and industrial controllers
Cloud-based training infrastructure
Edge deployment on embedded systems
Autonomous strategic report generation (Sakana Marlin)
Multi-agent LLM orchestration matching frontier models (Fugu Ultra)
Recursive Self-Improvement (RSI) Lab for autonomous AI research
Conductor system for natural language agent orchestration
Japan-based data residency and export control compliance
Finance multi-agent proposal generation with SMBC Group
AI-powered information analysis with DEEP DIVE
CoffeeBench benchmark for LLM agents in multi-agent economic environments
Autonomous model orchestration avoiding export control risks
Research-stage speech-to-speech AI with KAME architecture
