Real-Time Domain Adaptation Neural Network & Dual-Loop Residual Control for Sim-to-Real Robotics ( patent)

Tink Innovation Co., Ltd. is pleased to present this comprehensive commercialization proposal for its core proprietary patent:

"Robot Control System and Method Based on Real-Time Domain Adaptation Neural Network for Virtual Environment Based Sim-to-Real Data Mapping."

In modern robotics, Deep Reinforcement Learning (DRL) offers immense potential for autonomous skill acquisition. However, physical real-world trial-and-error causes severe hardware damage, safety hazards, and exorbitant costs. While training agents in ultra-precise simulation environments (e.g., Isaac Gym, MuJoCo) accelerates learning exponentially, physical robots inevitably encounter the "Reality Gap" (Domain Gap)—nonlinear discrepancies in friction coefficients, aerodynamic resistance, gear backlash, and motor inertia.

Proprietary Core Technological Breakthrough.

Our patented architecture resolves this fundamental bottleneck through a Dual-Loop Closed Architecture. By computing instantaneous residual error vectors between simulated nominal dynamics and real-world sensor feedback, a temporal neural network dynamically infers a Latent Physics Vector (zt). This vector simultaneously modifies real-time joint actuation commands via residual control and syncs back to calibrate the simulation environment.

Conventional Sim-to-Real approaches rely heavily on Domain Randomization (DR)—randomizing simulation parameters across wide bounds. While DR imparts basic robustness, it forces policies to converge toward overly conservative behaviors, degrading trajectory accuracy and energy efficiency.

Tink Innovation's patented mechanism overcomes this paradigm through an active Dual-Loop Closed Architecture.

Mathematical Formulation of the Latent Physics Estimator.

The system does not treat physical discrepancies as random noise. Instead, it frames the reality gap as a deterministic yet unobserved latent state variable vector zt, which encompasses dynamic surface friction variations, unmodeled drag coefficients, gear backlash compliance, and inertial distribution errors.

Why This Solves the Industry's Hardest Problem: Traditional robots fail when deployed in variable outdoor or heavy industrial environments because factory calibration degrades over time. Our Dual-Loop architecture guarantees that as mechanical wear occurs or floor conditions change, the robot autonomously self-tunes and updates its digital twin representation.