The article analyzes how the Dexterity‑BEV approach unifies heterogeneous robot sensor streams into a single bird's‑eye‑view coordinate system, aligning vision, state, action, and time to enable scalable, generalizable embodied AI, drawing parallels with the transformative impact of BEV in autonomous driving.
This survey systematically reviews Embodied AI security, proposing a five‑layer taxonomy (perception, cognition, planning, action & interaction, agentic system) that organizes over 400 papers on attacks, defenses, and open challenges, and highlights overlooked vulnerabilities such as multimodal perception fusion and planning instability under jailbreak attacks.
The 9th Tsinghua Big Data Intelligent Lecture gathered leading scholars and industry experts to showcase cutting‑edge research on multimodal perception, embodied intelligence, spatial AI, large‑model multimodal systems, and industrial time‑series databases, emphasizing their technical depth and real‑world impact.
This article explains how to use NVIDIA Isaac Lab and the TiledCamera component to run large‑scale, multimodal reinforcement learning on GPU clusters, covering environment setup, noVNC visualization, command‑line execution, distributed training with torchrun, and performance analysis across multiple GPU configurations.
ABot‑M0 tackles the three long‑standing "Babel Tower" challenges of embodied AI—data fragmentation, inconsistent representations, and training mismatches—by releasing the massive UniACT dataset, introducing Action Manifold Learning for direct action prediction, and designing a plug‑and‑play dual‑path perception architecture that outperforms prior models on multiple robot benchmarks.
Researchers from Beihang University present a high‑low UAV collaborative navigation paradigm, introducing the HaL‑13k dataset and AeroDuo framework, detailing high‑altitude planning with Pilot‑LLM, low‑altitude three‑stage search, and demonstrating superior target finding in complex environments.
In April 2018, Tsinghua University and Alibaba launched a joint Natural Interaction Experience Lab to explore next‑generation human‑machine interaction, aiming to give machines five senses and emotional understanding, and to apply this research across retail, smart homes, autonomous driving, and other emerging AI‑driven scenarios.
