Da-Qiang Guo

GraspTune: Tactile-Driven Execution Refinement for Robust Grasping

Visual grasp proposal generation has advanced rapidly, yet converting a selected proposal into a stable physical grasp remains a central execution-stage challenge. This paper introduces GraspTune, a tactile-driven execution-stage refinement framework that starts from a nominal proposal and applies bounded residual TCP...

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

Robot-GST: geometry-aware spatial-temporal robot policy representation and evaluation

Robotic-GST is presented, a geometry-aware spatio-temporal behaviour representation and evaluation framework that constructs a Gaussian-SAM robotic environment for real-to-sim policy verification and improves the reliability of real-world manipulation deployment.

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...