Beijing Institute of Technology
My research interests lie in embodied intelligence, with a focus on multimodal memory, retrieval-augmented reasoning, and robot navigation. I study how perceptual observations can be represented and retrieved to support decision-making in embodied systems.
- Multimodal representation and memory: semantic and spatial representations for open-world environments.
- Embodied reasoning and navigation: connecting high-level decisions with navigation and robot execution.
- Reproducible systems: experimental infrastructure for studying agent memory and robotic simulation.
Multimodal knowledge graph integration for open-world object-goal navigation. This project investigates the use of structured memory and multimodal retrieval to support navigation decisions.
Research focus
Visual observations are organized into semantic-spatial memory that can be queried during navigation. The work concerns the connection between environmental observations, retrieved evidence, and object-goal decisions.
World-model-assisted navigation and execution for the Go2-W platform, including map-independent charging and hybrid long-range navigation workflows.
System details and experimental materials
In the hybrid navigation workflow, Cosmos3-Edge selects an approved route, Nav2/RoamerX handles navigation, and DreamWaQ controls motion.
The September 10, 2026 simulation record covers stairs, ramps, and flat-ground obstacles with moving cylinder proxies. It reports 18 completed stages and 60.88 m of travel in MuJoCo / Unreal Engine, using sensor/model-fused obstacle data. These results describe the recorded simulation runs, rather than a real-robot benchmark.
An experimental framework for persona agents with explicit memory and retrieval, with adversarial smoke tests for inspecting agent behavior.
Related infrastructure
Isaac Sim Kitchen provides a reproducible Lightwheel Kitchen scene setup. Room contains related runtime work. These repositories support scripted setup, asset checks, and experimental inspection.
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