LLM-generated robot control code succeeds on 100%, 94.4%, and 88.9% of simple, composite, and complex instructions respectively¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? In simulation, LLM-generated control code achieved success rates of 100% for simple, 94.4% for composite, and 88.9% for complex instructions, with success declining as difficulty increases. → Shenqi Lu and Liangwei Zhang 2026

Evidence¶

Shenqi Lu and Liangwei Zhang 2026¶

Shenqi Lu and Liangwei Zhang. (2026). EduSim-LLM: An Educational Platform Integrating Large Language Models and Robotic Simulation for Beginners. https://arxiv.org/abs/2601.01196 doi:10.48550/arxiv.2601.01196

q2 · i? · design · r2

Dataset-based simulation evaluation of LLM-generated robot control code using Groq's llama-3.3-70b-versatile on three YouBot robots. The test set contained 108 instances across difficulty levels, with the article reporting "the success rates for simple, composite, and complex instructions are 100%, 94.4%, and 88.9%, respectively."

"Aggregated over 36 cases per group, the success rates for simple, composite, and complex instructions are 100%, 94.4%, and 88.9%, respectively, as shown in Fig. 6 (B). The success rate decreases as the instruction difficulty level increases from simple to composite and complex."

Discussion¶

Related Claims¶

• The article constructs an educational benchmark of 108 instruction instances grouped into simple, composite, and complex difficulty levels — related
• A single natural-language command can be decomposed by an LLM into coordinated sequential action sequences for multiple robots — related
• Participants rated the complex three-population scenario as more difficult than the simple one-population scenario — related