Motoniq's self-reflective VLA hits 92% on Perturbed LIBERO, beating RHO (53%) and Claude agent (9%)
hbouammar · x · 2026-10-23
Motoniq Research reports that having a model 'think about the VLA's thinking' substantially improves vision-language-action policies' capability, reliability, and cost-efficiency.
Across ten Perturbed LIBERO tasks, Motoniq achieves 92% mean success — 39 points above the next-best agent RHO (53%), with ASPIRE at 51%, CaP-Agent0 at 12%, and a Claude-based general frontier agent at just 9%. The authors also find Claude and GPT underperform their reputation in embodied AI, with improvements carrying over to baselines like LeWM.
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