  In robotic insertion tasks where the uncertainty exceeds the allowable
tolerance, a good search strategy is essential for successful insertion and
significantly influences efficiency. The commonly used blind search method is
time-consuming and does not exploit the rich contact information. In this
paper, we propose a novel search strategy that actively utilizes the
information contained in the contact configuration and shows high efficiency.
In particular, we formulate this problem as a Partially Observable Markov
Decision Process (POMDP) with carefully designed primitives based on an
in-depth analysis of the contact configuration's static stability. From the
formulated POMDP, we can derive a novel search strategy. Thanks to its
simplicity, this search strategy can be incorporated into a
Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are
realized through a low-level Cartesian Impedance Controller. Our method is
based purely on the robot's proprioceptive sensing and does not need visual or
tactile sensors. To evaluate the effectiveness of our proposed strategy and
control framework, we conduct extensive comparison experiments in simulation,
where we compare our method with the baseline approach. The results demonstrate
that our proposed method achieves a higher success rate with a shorter search
time and search trajectory length compared to the baseline method.
Additionally, we show that our method is robust to various initial displacement
errors.
