ACE-Brain-0.6 released: scaling embodied foundation models with verifiable spatial grounding supervision

ACE-Brain-0.6 combines robot manipulation recordings with complementary visual data from multiple sources through a unified data pipeline; after automatic annotation, the data is converted into verifiable grounding supervision, and Qwen3.5-9B is fine-tuned on about 1.5 million samples to produce the model. It outperforms GPT-6 Astra on four of seven public grounding benchmarks, with an overall average score of 75.41 versus Astra's 73.68. Under a fixed fine-tuning setup, aggregate grounding performance improves steadily as the dataset expands to cover more grounding scenarios.