Da-Qiang Guo

PredTac: Learning Contact-Rich Manipulation with Predicted Touch

Contact-rich manipulation benefits from tactile feedback, yet physical tactile sensors introduce hardware, calibration, synchronization, and maintenance costs that complicate policy learning and deployment. We formulate predicted touch as an alternative to measured tactile input and present PredTac, a framework that le...

Geometry-aware deep reinforcement learning for real-time integrated scheduling and nesting in additive manufacturing

Achieve What You Imagined: Learning to Align Actions with Visual Plans

World-action models can jointly predict future visual observations and robot actions. However, discrepancies may exist between their visual predictions and the consequences implied by generated actions. We observe that WAMs can often generate visually plausible task-completion outcomes before producing action sequences...