Pedro Uriguen Eljuri

• Doctor thesis explored Rearranging Tasks using a Monte Carlo Tree Search and Motion feasibility
• Research sponsored by the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) Scholarship Program.
• Co-founded Team NAIST, now Team NAIST-RITS-Panasonic, and successfully participated in the Airbus Shopfloor Challenge (ASC) and the Amazon Robotics Challenge (ARC).

• Master thesis explored Rearranging Tasks in Daily-life Environments using a Humanoid Robot, proposed a set of skills at the moment of organize an environment using a humanoid robot.
• Research sponsored by the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) Scholarship Program.
• Co-founded Team NAIST, now Team NAIST-RITS-Panasonic, and successfully participated in the Airbus Shopfloor Challenge (ASC) and the Amazon Robotics Challenge (ARC).

• Selected to conduct research on robotics in Japan through recommendation by the Japanese Embassy in Ecuador.

• Graduation thesis explored the design and construction of a device to analyze and record parameters of the water in rivers and lakes, collaboration of the Engineering School and Biology School of University of Azuay
• Introduced to a wide range of engineering disciplines with focus in electronics, telecommunications and mechanics
• Mandatory entrance examination in mathematics and physics for all the examinees.

• Graduated with diploma in physics and mathematics

Ranked 1st with Team NAIST-RITS-Panasonic in one of the three main tasks of the Future Convenience Store Challenge at the Trial Competition 2019 of World Robot Summit 2020.

Ranked 2nd with Team NAIST-RITS-Panasonic in one of the three main tasks of the Future Convenience Store Challenge at the Trial Competition 2019 of World Robot Summit 2020.

Awarded by the NEDO office for overall excellence in competition with Team NAIST-RITS-Panasonic during World Robot Summit 2018.

Awarded by the SICE society for displaying advanced research integration with Team NAIST-RITS-Panasonic during World Robot Summit 2018.

Ranked 1st and awarded 3,000,000 JPY with Team NAIST-RITS-Panasonic in one of the three main tasks of the Future Convenience Store Challenge at the World Robot Summit 2018.

Ranked 6th and awarded 10,000 USD with Team NAIST-Panasonic in the finals of the 2017 Amazon Robotics Challenge (formerly Amazon Picking Challenge) among 16 top international teams and 27 entries worldwide.

Winner of the biggest robotics challenge held at 2016 IEEE International Conference on Robotics and Automation (ICRA 2016) with a cash prize of 20,000 EUR.

Received the academic title of Doctor in Engineering after 3 years of research and contribution to the state of the art at the Robotics Laboratory of the Nara Institute of Science and Technology (NAIST).

The TOEIC IP is a standard test to measure the English reading and listening skills of people working in international environments. A score above 800 means an advanced command of the language.

Received the academic title of Master after 2 years of higher education in the Nara Institute of Science and Technology.

The TOEFL iBT test measures the ability to use and understand English at the university level by evaluating reading, listening, speaking, and writing skills in performing academic tasks. A score above 95 means an advanced command of the language.

Received the academic title of Engineer after 5 years of higher education at the Universidad del Azuay (University of Azuay).

In this paper, we address the problem of solving rearranging tasks using a robot. Rearranging tasks are challenging because they include many problems to solve at the same time, such as determining how to pick the items as well as planning how and where to place them. Solving a rearranging task usually consists of finding a set of pick-and-place instructions with a symbolic planner to perform the task. However, if the symbolic planner does not consider the robot's capability to execute the instructions, it will likely generate many infeasible instructions, which wastes time in multiple trials and failures. Therefore, we propose to combine symbolic and motion planning to confirm a sequence of instructions before its execution by the robot. To achieve this combination, we use a Motion Feasibility Checker (MFC), which selects a set of feasible poses for the robot from a feasibility database. The MFC verifies that the instructions of the symbolic planning are valid and searches for a pick-and-place pair of poses to execute the instructions. We use a Monte Carlo Tree Search (MCTS) as the symbolic planner, and we combine it with the MFC when creating or expanding the states in the tree. After the MCTS finds a set of instructions for the rearranging task, we execute those instructions with the robot. As these instructions were previously validated, the robot is able to execute them. The proposed method was tested in a simulation environment that reproduces the scenario of rearranging products on a shelf of a convenience store. The experiment results show that the proposed method outperforms the conventional method in various initial states of increasing levels of difficulty.

In this paper, we address the task of rearranging items with a robot. A rearranging task is challenging because one should solve the following issues: to determine how to pick the items and plan how and where to place the items. In this study, we focus on how to obtain a sequence of actions that the robot could execute reducing the failures when the motion planner creates the trajectory to move the robot, such as not finding a solution. To confirm the sequence of instructions before executing them with the robot, we combine a motion planner with a symbolic planner. For that purpose, we propose a Motion Feasibility Checker (MFC), which quickly decides if a given set of pick-and-place poses can be executed with respect to the robot’s kinematics. The MFC uses a database of possible pick and place poses of the target robot; given the initial and target pose of the item, the MFC finds a set of pick-and-place poses to execute that action with the robot. We use the Monte Carlo Tree Search (MCTS) to achieve a high performance of the symbolic planning. In the proposed method, the MCTS searches for the goal while it collaborates with the MFC. We tested the proposed method in a simulation environment doing a sandwich rearranging task in a convenience store setup.

Humans do rearranging tasks every day. These tasks are time-consuming and tedious. Rearranging tasks are challenging because there are many problems to solve, such as identifying the items, manipulating the items, and finding a strategy to rearrange that can be completed with the robot. In this work, we focus on how to execute rearranging tasks and avoid errors in the motion planner such as not finding a valid solution. We propose to combine the symbolic planner with the motion planner using a feasibility database to confirm if the instructions received by the symbolic planner are valid or not. The final poses of the robot end effector are previously validated with the motion planner, so we avoid sending the robot to invalid poses. The proposed method was tested in a simulation environment doing a sandwich rearranging task in a convenience store setup.

As the retail industry keeps expanding and shortage of workers increasing, there is a need for autonomous manipulation of products to support retail operations. The increasing amount of products and customers in establishments such as convenience stores requires the automation of restocking, disposing and straightening of products. The manipulation of products needs to be time-efficient, avoid damaging products and beautify the display of products. In this paper, we propose a robotic system to restock shelves, dispose expired products, and straighten products in retail environments. The proposed mobile manipulator features a custom-made end effector with compact and compliant design to safely and effectively manipulate products in retail stores. Through experiments in a convenience store scenario, we verify the effectiveness of our system to restock, dispose and rearrange items.

Recent demographic trends in super aging societies, such as Japan, is leading to severe worker shortage. Service robots can play a promising role to augment human workers for performing various household and assistive tasks. Toilet cleanup is one such challenging task that involves performing complaint motion planning in a constrained toilet setting. In this study, we propose an end-to-end robotic framework to perform various tasks related to toilet cleanup. Our key contributions include the design of a complaint and multipurpose end-effector, an adaptive motion generation algorithm, and an autonomous mobile manipulator capable of garbage detection, garbage disposal and liquid removal. We evaluate the performance of our framework with the competition setting used for toilet cleanup in the Future Convenience Store Challenge at the World Robot Summit 2018. We demonstrate that our proposed framework is capable of successfully completing all the tasks of the competition within the time limit.

The Amazon Robotics Challenge (ARC) has become one of the biggest robotic competitions in the field of warehouse automation and manipulation. In this paper, we present our solution to the ARC 2017 which uses both learning-based and feature-based techniques for object recognition and grasp point estimation in unstructured collections of objects and a partially controlled space. Our solution proved effective both for previously unknown items even with little data acquisition, as well as for items from the training set, obtaining the 6th place out of 16 contestants.

In this letter, we present an end effector that can drill holes compliant to aeronautic standards while mounted on a lightweight robot arm. There is an unmet demand for a robotic solution capable of drilling inside an aircraft fuselage, as size, weight, and space constraints disqualify current commercial solutions for this task. Our main contribution is the mechanical design of the end effector with high-friction, vibration-reducing feet that are pressed against the workpiece during the drilling process to increase stability, and a separate linear actuator to advance the drill. This relieves the robot arm of the task of advancing and stabilizing the drill, and leaves it with the task of positioning and holding the end effector. The stabilizing properties of the end effector are confirmed experimentally. The solution took first place at the Airbus Shopfloor Challenge, an international robotics competition held at ICRA 2016 that modeled the in-fuselage drilling task.

The Amazon Robotics Challenge has become one of the biggest robotic challenges in the field of warehouse automation and manipulation. In this paper, we present an overview of materials available for newcomers to the challenge, what we learned from the previous editions and discuss the new challenges within the Amazon Robotics Challenge 2017. We also outline how we developed our solution, the results of an investigation on suction cup size and some notable difficulties we encountered along the way. Our aim is to speed up development for those who come after and, as first-time contenders like us, have to develop a solution from zero.

We propose a complete, modular robotic solution for industrial drilling tasks in an aircraft fuselage. The main contribution is a custom-made end effector with vibration-reducing feet that rest on the workpiece during the drilling process to increase stability. The solution took 1st place at the Airbus Shopfloor Challenge, an international robotics competition held at ICRA 2016.

The industry's recent growing interest in virtual reality, augmented reality and smart wearable devices has created a new momentum for eye tracking. Eye movements in particular are viewed as a way to obtain natural user responses from wearable devices alongside gaze information used to analyze interests and behaviors. This paper extends our previous work by introducing a wearable eye-tracking device that enables the reconstruction of 3D eye models of each eye from two RGB cameras. The proposed device is built using high-resolution cameras and a 3D-printed frame attached to a pair of JINS MEME glasses. The 3D eye models reconstructed from the proposed device can be used with any model-based eye-tracking approach. The proposed device is also capable of extracting scene information from the cornea reflections captured by the cameras, detecting blinks from an electrooculography sensor as well as tracking head movements from an accelerometer combined with a gyroscope.