Yifei Yao

ARIEL: An Agent-Ready Humanoid Behavior Model with In-Context Execution Memory

Research project 2026 · In development

ARIEL gives an agent a simple interface to whole-body control. Choose a demonstration, load it into memory, then recall the motion or steer its style with velocity and heading commands. The controller keeps executing between agent decisions.

Why agent-ready?

These interfaces connect intermittent agent decisions to continuous motor control. Robustness to delays and abrupt command changes is still being evaluated.

ARIEL builds on GBC with a 4.24M-parameter controller. Its shared Transformer and fixed-size memory provide a path to more capacity and motion data; scaling experiments are ongoing.

Deployment measurements

An earlier streaming prototype measured 1.09 ms median inference with 4.21M weights, versus 13.92 ms and 49.73M weights for SONIC v1.1: about 12.7× lower latency and 11.8× fewer weights on the same Xeon 8358P CPU, using single-thread ONNX Runtime, 300 warm-up calls and 3,000 timed calls. These are historical measurements; deployment timing for the current memory controller is being evaluated separately.

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