Haojian Huang

Counterfactual outcomes reveal that existing embodied agents miss 20% of potential success by not exploring alternative skill branches.

Frontier VLMs excel at precise scene estimation but falter in executing coordinated, goal-directed actions, highlighting a critical bottleneck for robot generalism.

Mainstream video models consistently falter in fine-grained understanding, revealing critical vulnerabilities in their hallucination capabilities.

Long-horizon embodied agents struggle to translate long-term memory into actionable plans, exposing critical gaps in current benchmarks and methodologies.

VLMs exhibit distinct failure modes under physical visual stress, revealing that traditional accuracy metrics can mask critical vulnerabilities in embodied AI systems.

Open-source models can now rival GPT-4 in spatial reasoning, thanks to a novel two-stage training framework that grounds LLMs in topological anchors.