Research agenda
Active embodied intelligence is an evidence problem.
I study active embodied intelligence under partial observability, with a focus on active perception, world models, and self-improving agents. My current work is grounded in UAV autonomy and inspection.
研究不完整观测下的主动具身智能，重点关注主动感知、世界模型与自进化智能体；当前工作以无人机自主与巡检为主要落地场景。
Active Perception for UAV Inspection
A drone rarely begins with all the evidence an inspection task requires. The research question is how to coordinate wide-field sensing, a high-resolution gimbal, and body motion while respecting viewpoint, bandwidth, latency, and safety constraints.
The goal is a policy that can decide what evidence is missing, where to acquire it, and when the evidence is sufficient. This is a research agenda, not a claim of a completed benchmark or deployed system.
Self-evolving Simulation and Data Engines
Evaluation should drive data collection. As an Embodied AI Researcher at Avant Robotics, I work on feedback loops that expose failure modes in embodied UAV tasks, collect targeted interaction data around those failures, and use the resulting evidence to improve navigation and action models.
The team system produces 39 million valid simulated interaction steps per month, improves sampling throughput by 3× on a single RTX 5090, and supports a 0.8B world/action model reporting 74% navigation-and-avoidance success. These figures describe the integrated team system rather than an individual result.
The emphasis is on task coverage, repeatability, and real-world grounding. Specific scene sources and named locations remain private.
Reliable Multimodal Intelligence under Partial Observability
My earlier work asks how models can learn when views or modalities are missing. That foundation now supports a broader embodied question: how should an agent reason and act when observations are incomplete, uncertain, or viewpoint-dependent?