GPT-6 Astra as a Quadruped Locomotion Policy: 250 Inferences, 5 Seconds of Walking
YuXiang_IRVL · x · 2026-09-09
Responding to a professor's challenge that an LLM could directly implement a quadruped locomotion policy, Srinivas ran the experiment with GPT-6 Astra: the model output joint targets at 50 Hz like an RL policy, controlling a simulated Unitree Go1 with physics paused between calls. 250 inferences produced 5 seconds of walking — a playful test of whether frontier models, if orders of magnitude faster and cheaper, could serve as edge-device control policies.
Related event: GPT-6 Astra Directly Controls Quadruped Locomotion(2 posts)→
More from Embodied
- Physical world agent swarm demo shared on X — pduan · 2026-10-26
- Looking for a wearable mic with best battery life that feeds recordings to personal agents via MCP — altryne · 2026-10-26
- 'Lamp Swarm': a desk of lamp-shaped robots shows what physical AI work looks like — dee_hw · 2026-10-26
- Tencent's HiSTEVE 1.0: hierarchical VLA agent plays Minecraft with native keyboard and mouse, hits 85% SOTA — _akhaliq · 2026-10-26
- SJTU's VaM lets robots learn online from mistakes, 95%+ success over 4-hour autonomous runs — chris_j_paxton · 2026-10-26
- DIY humanoid PhoneGirl day 4: collision tests, servo rework, 160° knee bend — _Stocko_ · 2026-10-26