Akshay Parkhi's Weblog

GEAR-SONIC

21st February 2026

GEAR-SONIC (Supersizing Motion Tracking for Natural Humanoid Whole-Body Control) is the big upgrade over the Decoupled WBC approach in the GR00T stack. It’s a completely different approach to humanoid control. It controls the whole body as one unit, trained on human motion data rather than hand-crafted reward functions.

Decoupled WBC vs SONIC

Aspect	Decoupled WBC	SONIC
Controls	Legs only (15 joints), arms locked	Whole body — all joints including arms
Trained on	RL reward functions in Isaac Sim	Large-scale human motion data
Input	Simple commands (velocity, height)	Full-body motion references from video/mocap
Architecture	Separate lower body RL + upper body IK	Single unified policy
Used in	GR00T N1.5 / N1.6	Latest generation

The Decoupled WBC splits the problem. One RL policy handles the legs. Inverse kinematics handles the arms. The two are then stitched together. SONIC replaces all of that with a single neural network that controls every joint simultaneously, trained by watching how humans move.

What Makes SONIC Different

The core idea: instead of designing reward functions that describe what “good walking” looks like, SONIC learns directly from large-scale human motion capture data. The policy takes a full-body motion reference as input and tracks it with the robot’s body. This is the reason for the name “Supersizing Motion Tracking.”

This means the robot can do things that are nearly impossible to specify with reward functions:

• Natural arm swing while walking
• Coordinated whole-body gestures
• Human-like weight shifting and balance recovery
• Smooth transitions between different movement types

Repository Structure

The SONIC codebase has distinct stages:

GR00T-WholeBodyControl/
├── sonic_policy/    → Policy training (Isaac Sim + RL)
├── sonic_retarget/  → Human motion → robot motion conversion
├── sonic_deploy/    → C++ inference for real hardware deployment

sonic_retarget is the bridge between human motion data and robot-compatible references. It maps the kinematics of a human skeleton to the Unitree G1’s joint configuration. sonic_policy trains the tracking policy in Isaac Sim. sonic_deploy packages it for real-time C++ inference on the actual robot.

Links

• Paper: arxiv.org/abs/2511.07820
• Model weights: huggingface.co/nvidia/GEAR-SONIC
• Docs: nvlabs.github.io/GR00T-WholeBodyControl