  Efficiency and reliability are critical in robotic bin-picking as they
directly impact the productivity of automated industrial processes. However,
traditional approaches, demanding static objects and fixed collisions, lead to
deployment limitations, operational inefficiencies, and process unreliability.
This paper introduces a Dynamic Bin-Picking Framework (DBPF) that challenges
traditional static assumptions. The DBPF endows the robot with the reactivity
to pick multiple moving arbitrary objects while avoiding dynamic obstacles,
such as the moving bin. Combined with scene-level pose generation, the proposed
pose selection metric leverages the Tendency-Aware Manipulability Network
optimizing suction pose determination. Heuristic task-specific designs like
velocity-matching, dynamic obstacle avoidance, and the resight policy, enhance
the picking success rate and reliability. Empirical experiments demonstrate the
importance of these components. Our method achieves an average 84% success
rate, surpassing the 60% of the most comparable baseline, crucially, with zero
collisions. Further evaluations under diverse dynamic scenarios showcase DBPF's
robust performance in dynamic bin-picking. Results suggest that our framework
offers a promising solution for efficient and reliable robotic bin-picking
under dynamics.
