We present a dynamic robotic packing approach for autonomously and densely packing unknown objects that arrive continually at a packing site, such as those delivered via a conveyor belt. Our system enables simultaneous online object perception, rapid object modeling, dense packing planning, and object picking and packing in the presence of multi-modal sensing uncertainties, approximations in rapid object modeling, and robot motion inaccuracies. The method can accommodate both rigid and certain non-rigid objects, including in scenes where items are piled. Our approach is validated in real experiments with packing tasks involving unknown objects of various shapes and types. Experimental results demonstrate the effectiveness and efficiency of the introduced method in achieving high-density packing for different kinds of objects.

This paper addresses human-robot collaborative (HRC) precision assembly that complements natural human ability and the strength of an autonomous robot system. Our approach enables both flexibility and efficiency of tight-clearance assembly of various complex-shaped parts in the presence of uncertainty without requiring assembly skills and knowledge of robotics from the human operator. We demonstrated the effectiveness of our approach in a variety of experiments and comparisons with other HRC assembly approaches.

In this paper, we propose a novel and general method for autonomous robotic assembly of arbitrary and complex-shaped parts in the presence of 6-dimensional uncertainty. When a nominal assembly motion of the robot holding a part is stopped by contact due to uncertainty, our method finds the best estimate for the uncertainty and the contact configuration of the part based on sensed force/torque and uses that information to find a more accurate estimate of the goal configuration to guide a recovery motion of the part. It is based on a general, surface-based sphere tree representation of parts, a constrained optimization strategy to find the best estimate of the contact configuration under an uncertainty estimate, and a learned force/torque calibration model to relate computed force/torque and the sensed real force/torque. The method is applied and evaluated on different complex-shaped multi-peg-in-hole tasks. The results show that our method can achieve successful assembly with the presence of realistic 6-D uncertainties more than 10 times of the tight task clearances in terms of orientation clearance$(< 0.015rad)$and position clearance$(< 1.5mm)$, in all the test cases.