Kailash Labs

Those building or deploying general-purpose robot models (VLAs and frontier multimodal models) lack honest, real-world measures of what these models can actually do. Today demos are cherry-picked and simulation benchmarks saturate, so results that look good in a lab often fail to survive contact with a real workplace. Anyone deciding which model to train or deploy currently has no trusted reference for actual robot capability, only curated showcases and benchmarks that do not reflect real conditions.

Kailash Labs deploys general-purpose robot models on real robots performing economically useful work, running many tasks across many environments and many trials. It measures where these models succeed and where they break, keeping a careful record of failure modes. Every failure and every human recovery is turned into structured data, which is used to supply the real-world experience these generalist models are missing. The long-term aim is to let anyone with any robot and any task pick a capable model, collect a handful of demonstrations, and get back a policy they can trust.

The text argues generalist VLA and multimodal models are becoming the default the way language models did, replacing one-specialist-model-per-task approaches, making honest real-world evaluation newly urgent.