Lijun Wang

Dual-arm robots can generalize to unseen task compositions by combining skill-conditioned parameters and arm-wise attention, achieving 39% success on physical hardware.

Grounding policy optimization in canonical disease graphs allows a compact 9B model to outperform GPT-5.5 on long-tail clinical diagnosis, revealing that controlled evidence routing matters far more than raw retrieval volume.

This work proposes CARE (Corrective Atomic Robotic Execution), a framework that improves recovery by learning from failures encountered during execution, and introduces the Failure State Recovery Benchmark (FSR-Bench), which evaluates recovery from intermediate failure states under local deviations and structural anomalies.