A Robust and Efficient Robotic Packing Pipeline with Dissipativity- Based Adaptive Impedance-Force Control

For humans, dense bin packing heavily relies on force perception. However, current robotic packing studies only focus on the visual input or adopt auxiliary push-to-place actions to eliminate gaps, suffering from high time expenditure and poor robustness. To address such limitations, we first introduce a novel external force estimation method based on the generalized momentum observer, which can avoid the influence of joint acceleration noises and achieve real-time high-precision monitoring. Second, to obtain compliant interaction and fine robustness, an adaptive variable impedance policy is developed to track dynamic motion and desired force, and compensate for uncertainties. Meanwhile, we perform dissipativity analysis and a virtual energy supply function is augmented to the system for optimization, providing a solid foundation for stability. Third, we propose an efficient packing methodology with three sub- tasks by considering the distinct interaction and constraint states in different areas. Our packing strategies eliminate the need for subsequent auxiliary actions and are proven to enhance efficiency. We perform quantitative evaluations to verify our external force estimation method, conduct comparison studies with current packing methods, and investigate the contribution of our dissipativity-based adaptive controller. The superior results not only prove the robustness and efficiency of our pipeline, but also pave the way for practical applications of packing.