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Humanoid robots are a promising platform for general-purpose manipulation. Recent Vision-Language-Action (VLA) policies learn actions directly from multimodal observations, while World Action Models (WAMs) further incorporate future visual prediction to improve action generation. However, in hierarchical humanoid syste...
SocialOmni is introduced, an offline diagnostic benchmark that separates three turn-level decisions: identifying who is speaking, deciding when a designated participant should enter at an annotated query time, and determining how that participant should continue the dialogue.
FluxVLA Engine is presented, an open, configuration-driven platform that turns heterogeneous embodied-policy components into a reproducible data-to-deployment workflow and targets the engineering bottlenecks separating promising embodied-learning algorithms from reproducible evaluation and dependable deployment.
Grounding action, verification, and memory in state, what the authors call state-grounding, shifts the main bottleneck from perception toward reasoning: failures depend more on what the agent thinks than on what it sees.
This work trains software-engineering, deep-research, and general tool-use agents on gpt-oss-20b and improves task reward in all three and presents MCP-Universe RL (MCP-U RL), an open-source framework that takes over both.
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