Yang Gao

Yufeng Liu, Hang Yu, Juntu Zhao et al.

Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories that are not intrinsically smooth. We propose Legato, a training-time continuation method for action-chunked flow-based VLA policies. Specifically, Legato initializes denoising from a schedule-shaped mixture of known actions and noise, exposing the model to partial action information. Moreover, Legato reshapes the learned flow dynamics to ensure that the denoising process remains consistent between training and inference under per-step guidance. Legato further uses randomized schedule condition during training to support varying inference delays and achieve controllable smoothness. Empirically, Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time. Extensive real-world experiments show that Legato consistently outperforms RTC across five manipulation tasks, achieving approximately 10% improvements in both trajectory smoothness and task completion time.

YukTungSamuel Fang, Zhikang Shi, Jiabin Qiu et al.

Driven by recent advancements in foundation models, semantic scene graphs have emerged as a promising paradigm for high-level 3D environmental abstraction in robot navigation. However, existing frameworks struggle to successfully handle complex embodied queries while ensuring continuous semantic graph construction. To address these limitations, we present INHerit-SG, an asynchronous dual-stream architecture that systematically structures the 3D environment into a RAG-ready knowledge base. Specifically, our framework integrates comprehensive node representations, an event-triggered asynchronous update scheme, and a structured retrieval mechanism. While geometric segmentation is decoupled from semantic reasoning to maintain mapping efficiency, the semantic nodes also store natural language summaries to support text-based retrieval. Furthermore, we propose an interpretable retrieval pipeline that couples the reasoning capabilities of multi-role LLMs with the topological structure of the scene graph, followed by a visual verification process to mitigate false positives. We evaluate INHerit-SG on a newly constructed benchmark for complex embodied semantic query retrieval, HM3DSem-SQR, and in real-world environments. Experiments demonstrate that our system achieves state-of-the-art performance on complex queries, especially for those involving negations and chained spatial constraints. Project Page: https://fangyuktung.github.io/INHeritSG.github.io/