Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning

Modern robot navigation systems encounter difficulties in diverse and complex indoor environments. Traditional approaches rely on multiple modules with small models or rule-based systems and thus lack adaptability to new environments. To address this, we developed Astra, a comprehensive dual-model architecture, Astra-Global and Astra-Local, for mobile robot navigation. Astra-Global, a multimodal LLM, processes vision and language inputs to perform self and goal localization using a hybrid topological-semantic graph as the global map, and outperforms traditional visual place recognition methods. Astra-Local, a multitask network, handles local path planning and odometry estimation. Its 4D spatial-temporal encoder, trained through self-supervised learning, generates robust 4D features for downstream tasks. The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot. Deployed on real in-house mobile robots, Astra achieves high end-to-end mission success rate across diverse indoor environments.

RadarLLM: Empowering Large Language Models to Understand Human Motion from Millimeter-wave Point Cloud Sequence

Millimeter-wave radar provides a privacy-preserving solution for human motion analysis, yet its sparse point clouds pose significant challenges for semantic understanding. We present Radar-LLM, the first framework that leverages large language models (LLMs) for human motion understanding using millimeter-wave radar as the sensing modality. Our approach introduces two key innovations: (1) a motion-guided radar tokenizer based on our Aggregate VQ-VAE architecture that incorporates deformable body templates and masked trajectory modeling to encode spatiotemporal point clouds into compact semantic tokens, and (2) a radar-aware language model that establishes cross-modal alignment between radar and text in a shared embedding space. To address data scarcity, we introduce a physics-aware synthesis pipeline that generates realistic radar-text pairs from motion-text datasets. Extensive experiments demonstrate that Radar-LLM achieves state-of-the-art performance across both synthetic and real-world benchmarks, enabling accurate translation of millimeter-wave signals to natural language descriptions. This breakthrough facilitates comprehensive motion understanding in privacy-sensitive applications like healthcare and smart homes. We will release the full implementation to support further research on https://inowlzy.github.io/RadarLLM/.