Yun-Sun Oh

A core capability of robots is to reason about multiple objects under uncertainty. Partially Observable Markov Decision Processes (POMDPs) provide a means of reasoning under uncertainty for sequential decision making, but are computationally intractable in large domains. In this paper, we propose Object-Oriented POMDPs (OO-POMDPs), which represent the state and observation spaces in terms of classes and objects. The structure afforded by OO-POMDPs support a factorization of the agent's belief in

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show th

This article introduces a robust and safe path planning algorithm in order to satisfy mission requirements specified in linear temporal logic (LTL). When a path is planned to accomplish a mission, it is possible for a robot to fail to complete the mission or collide with obstacles due to noises and disturbances in the system. Hence, we need to find a robust path against possible disturbances. We introduce a robust path planning algorithm, which maximizes the probability of success in accomplishi

Each person has a different personal space and behaves differently when another person approaches. Based on this observation, we propose a novel method to learn how to approach a person comfortably based on the person's preference while avoiding uncomfortable encounters. We propose a personal comfort field to learn each person's preference about an approaching object. A personal comfort field is based on existing theories in anthropology and personalized for each user through repeated encounters

This paper presents a robust target tracking algorithm for a mobile sensor with a fan-shaped field of view and finite sensing range. The goal of the mobile robot is to track a moving target such that the probability of losing the target is minimized. We assume that the distribution of the next position of a moving target can be estimated using a motion prediction algorithm. If the next position of a moving target has the Gaussian distribution, the proposed algorithm can guarantee the tracking su

In the field of robot manipulation, learnable task planners are gaining attention, especially for long-horizon tasks such as cooking. However, existing methods that predominantly rely on symbolic representations suffer from limitations in generalization capabilities, particularly in handling unseen objects. Given that objects may vary in real-world environments, this limitation may constrain their practical applicability. To address this issue, we propose a novel task-planning framework that lev

We investigate a path planning algorithm for generating robust and safe paths, which satisfy mission requirements specified in linear temporal logic (LTL). When robots are deployed to perform a mission, there can be disturbances which can cause mission failures or collisions with obstacles. Hence, a path planning algorithm needs to consider safety and robustness against possible disturbances. We present a robust path planning algorithm, which maximizes the probability of success in accomplishing

This paper proposes a telerobotic system based on a smartphone and Nao, a humanoid robot from Aldebaran Robotics. A user can control the robot using her smartphone and interact with people and surroundings around the robot in a remote location. The overall system includes two servers to facilitate the connection between the user's smartphone and the robot. We have particularly focused on providing a user-friendly interface such that a user who is unfamiliar with the robot platform can control th

This paper presents the method of trajectory planning by the robot end-effector which accounts for more accurate and smooth differential geometry of the ruled surface generated by tool line fixed with end-effector based on the methods of curvature theory of ruled surface and the dual curvature theory, and focuses on the underlying relation to unite them for enhancing the efficiency for trajectory planning. Robot motion can be represented as motion properties of the ruled surface generated by tra

This paper presents a mobile robot system where the robot tracks a moving target. The system minimizes the probability of losing the target. If the next position of a moving target has the Gaussian distribution, the proposed system guarantees the tracking success probability. In addition, we minimize the moving distance of the mobile robot based on the chosen bound on the tracking success probability. We built a well-designed system on Robot Operating System for applications such as a smart shop

Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction. The example command "go to the kitchen before going to the second floor" contains spatial abstraction, given that "floor" consists of individual rooms that can also be referred to in isolation ("kitchen", for example). There is also a temporal ordering of events, defined by the word "before". Previous works have used Linear Temporal Logic (LTL) to interpret temporal language (suc

We propose a robust target tracking algorithm for a mobile robot under identity uncertainty, which arises in crowded environments. When a mobile robot has a sensor with a fan-shaped field of view and finite sensing region, the proposed algorithm aims to minimize the probability of losing a moving target. We predict the next position of a moving target in a crowded environment using a multiple-hypothesis prediction algorithm which combines the motion model and appearance model of the target. When