Built at Fireworks: Deploy with Microsoft & NVIDIA

Joint Tuner

A small open model that tunes PID controllers for robot joints in a handful of physical tests. Problem: every robot joint (arm elbow, humanoid ankle, drone roll axis) runs a PID loop that must be tuned. Today that means repeated step tests on real hardware: time, wear, risk. Approach: propose, simulate, repair. 1. A fine-tuned open model proposes 8 PID gain sets in one call. 2. A control-theory judge checks gain margin, phase margin, settling time, overshoot, steady-state error. 3. If none pass, Nelder-Mead polishes the best ones under a fixed test budget. Fireworks: Qwen3.6-35B-A3B LoRA fine-tuned with Fireworks serverless training on 7,500 simulator-verified designs ($5.11). On 100 held-out ControlEval tasks: first answer 20 to 50, best of 8 53 to 81. Median latency 1.35 s. Results (same 100 held-out ControlEval tasks): - With 8 step tests: 76 solved vs 27 for optimizer only (+49 pts, 95% CI 38 to 59, McNemar p = 3e-13). Significant at every budget from 1 to 256. - Median tests to first success: 1 vs 19.5. - Overall 90/100 vs GPT-6.1 Terra 89/100 (not significantly different), at about 1 s vs 19 s of model time. - Robustness to 20% plant mismatch: 83% still meet spec vs 68% for optimizer only; GPT-6.1 Terra is better at 94%, reported as a weakness. Demo: MuJoCo renders of an actuator, robot arm elbow, humanoid ankle and drone roll axis, each driven by the tuned gains, plus a three-way tuning race. Repo includes raw per-task results, stats scripts, and the approaches that lost.