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Abstract-This paper introduces autonomous sorting of moving coins via a robot as a multi-level task that supports the principled study of robotics fundamentals including kinematics, dynamics, perception, motion planning, controls, and optimization based around a widely obtainable, standardized, low-cost object (a coin). The paper also presents a demonstrated solution to this in the form of the CHARM (Coin Handling Arm for Robotics Mastery) robot, which addresses the autonomous coin sorting problem using an economical kit made from commodity computing hardware and three Dynamixel servomotors. From a learning perspective, this problem facilitates interdisciplinary practice across subject and grade levels with an algorithmic foundation that is central to modern robotics. Evidence supporting this approach is illustrated from case studies of student projects and, in particular, the CHARM robot. Beyond practice alone, by presenting a challenging (but manageable) research problem, we found that the coin sorting task teaches robotics in a principled way. Further, algorithmic complexity tiers the problem to academic levels. While motivated by robotics education, the (optimal) coin sorting problem may also be seen as an archetype problem for manipulation/motion-planning research. Thus, this also promotes a research foundation supporting later research opportunities.

An autonomous robot must decide a good strategy to achieve its long term goal, despite various types of uncertainty. The Partially Observable Markov Decision Processes (POMDPs) is a principled framework to address such a decision making problem. Despite the computational intractability of solving POMDPs, the past decade has seen substantial advancement in POMDP solvers. This paper presents our experience in enabling on-line POMDP solving to become the sole motion planner for a robot manipulation demo at IEEE SIMPAR and ICRA 2018. The demo scenario is a candy-serving robot: A 6-DOFs robot arm must pick-up a cup placed on a table by a user, use the cup to scoop candies from a box, and put the cup of candies back on the table. The average perception error is ∼3cm (≈ the radius of the cup), affecting the position of the cup and the surface level of the candies. This paper presents a strategy to alleviate the curse of history issue plaguing this scenario, the perception system and its integration with the planner, and lessons learned in enabling an online POMDP solver to become the sole motion planner of this entire task. The POMDP-based system were tested through a 7 days live demo at the two conferences. In this demo, 150 runs were attempted and 98% of them were successful. We also conducted further experiments to test the capability of our POMDP-based system when the environment is relatively cluttered by obstacles and when the user moves the cup while the robot tries to pick it up. In both cases, our POMDP-based system reaches a success rate of 90% and above.

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