Papers

These are my most recent papers as first author. For the full list, check my Google Scholar.

Overview of DriftQL: sampling states and noise, forming positives and negatives, computing the conditional drift field, and the drift plus Q-learning loss

Drift Q-Learning

NeurIPS 2026

DriftQL learns a single drift field, balancing attraction toward the dataset with repulsion for diversity, so an offline policy can act in one forward pass, no denoising chain or ODE solver required. It matches diffusion and flow policies on D4RL and OGBench and stays far more robust when the data is corrupted.

Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion

CoRL 2026

QWM conditions one world model on scale-invariant physical features and trains every policy inside it. Given the same morphology spec, a model-free policy collapses on unseen robots while QWM transfers zero-shot to new quadrupeds, in simulation and on hardware.