Seungchan Kim

ICRA Conference 2025 Conference Paper

MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions

• Cherie Ho
• Seungchan Kim
• Brady G. Moon
• Aditya Parandekar
• Narek Harutyunyan
• Chen Wang 0033
• Katia P. Sycara
• Graeme Best

Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on structured indoor environments, which often exhibit predictable, repeating patterns. Conventional frontier-based exploration approaches have difficulty leveraging this predictability, relying on simple heuristics such as ‘closest first’ for exploration. More recent deep learning-based methods predict unknown regions of the map for information gain computation, but these approaches are often sensitive to the predicted map quality or fail to account for sensor coverage. To overcome these issues, our key insight is to jointly reason over what the robot can observe and its uncertainty to calculate probabilistic information gain. We introduce MapEx, a new exploration framework that uses predicted maps to form probabilistic sensor model for information gain estimation. MapEx generates multiple predicted maps based on observed information, and takes into consideration both the computed variances of predicted maps and estimated visible area to estimate the information gain of a given viewpoint. Experiments on the real-world KTH dataset showed on average 12. 4% improvement than representative map-prediction based exploration and 25. 4% improvement than nearest frontier approach. Website: https://mapex-explorer.github.io/

IROS Conference 2025 Conference Paper

PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration

• Seungjae Baek
• Brady G. Moon
• Seungchan Kim
• Muqing Cao
• Cherie Ho
• Sebastian A. Scherer
• Jeong hwan Jeon

Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Website: pipe-planner.github.io

IROS Conference 2025 Conference Paper

RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration

• Omar Alama
• Avigyan Bhattacharya
• Haoyang He
• Seungchan Kim
• Yuheng Qiu
• Wenshan Wang
• Cherie Ho
• Nikhil Varma Keetha

Open-set semantic mapping is crucial for openworld robots. Current mapping approaches either are limited by the depth range or only map beyond-range entities in constrained settings, where overall they fail to combine within-range and beyond-range observations. Furthermore, these methods make a trade-off between fine-grained semantics and efficiency. We introduce RayFronts, a unified representation that enables both dense and beyond-range efficient semantic mapping. RayFronts encodes task-agnostic openset semantics to both in-range voxels and beyond-range rays encoded at map boundaries, empowering the robot to reduce search volumes significantly and make informed decisions both within & beyond sensory range, while running at 8. 84 Hz on an Orin AGX. Benchmarking the within-range semantics shows that RayFronts’s fine-grained image encoding provides 1. 34× zero-shot 3D semantic segmentation performance while improving throughput by 16. 5×. Traditionally, online mapping performance is entangled with other system components, complicating evaluation. We propose a planner-agnostic evaluation framework that captures the utility for online beyond-range search and exploration, and show RayFronts reduces search volume 2. 2× more efficiently than the closest online baselines.

IROS Conference 2022 Conference Paper

Robotic Interestingness via Human-Informed Few-Shot Object Detection

• Seungchan Kim
• Chen Wang 0033
• Bowen Li 0007
• Sebastian A. Scherer

Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can adapt to environments and detect interesting scenes quickly, but lack the ability to adapt to human-informed interesting objects. To solve this problem, we introduce a human-interactive framework, AirInteraction, that can detect human-informed objects via few-shot online learning. To reduce the communication bandwidth, we first apply an online unsupervised learning algorithm on the unmanned vehicle for interestingness recognition and then only send the potential interesting scenes to a base-station for human inspection. The human operator is able to draw and provide bounding box annotations for particular interesting objects, which are sent back to the robot to detect similar objects via few-shot learning. Only using few human-labeled examples, the robot can learn novel interesting object categories during the mission and detect interesting scenes that contain the objects. We evaluate our method on various interesting scene recognition datasets. To the best of our knowledge, it is the first human-informed few-shot object detection framework for autonomous exploration.

IJCAI Conference 2019 Conference Paper

DeepMellow: Removing the Need for a Target Network in Deep Q-Learning

• Seungchan Kim
• Kavosh Asadi
• Michael Littman
• George Konidaris

Deep Q-Network (DQN) is an algorithm that achieves human-level performance in complex domains like Atari games. One of the important elements of DQN is its use of a target network, which is necessary to stabilize learning. We argue that using a target network is incompatible with online reinforcement learning, and it is possible to achieve faster and more stable learning without a target network when we use Mellowmax, an alternative softmax operator. We derive novel properties of Mellowmax, and empirically show that the combination of DQN and Mellowmax, but without a target network, outperforms DQN with a target network.

RLDM Conference 2019 Conference Abstract

DeepMellow: Removing the Need for a Target Network in Deep Q-Learning

• Seungchan Kim
• Kavosh Asadi
• George Konidaris

Deep Q-Network (DQN) is a learning algorithm that achieves human-level performance in high- dimensional, complex domains like Atari games. One of the important elements in DQN is its use of target network, which is necessary to stabilize learning. We argue that using a target network is incompatible with online reinforcement learning, and it is possible to achieve faster and more stable learning without a target network, when we use an alternative action selection operator, Mellowmax. We present new mathematical properties of Mellowmax, and propose a new algorithm, DeepMellow, which combines DQN and Mellow- max operator. We empirically show that DeepMellow, which does not use a target network, outperforms DQN with a target network.

AAMAS Conference 2019 Conference Paper

Removing the Target Network from Deep Q-Networks with the Mellowmax Operator

• Seungchan Kim
• Kavosh Asadi
• Michael Littman
• George Konidaris

Deep Q-Network (DQN) is a learning algorithm that achieves humanlevel performance in high-dimensional domains like Atari games. We propose that using an softmax operator, Mellowmax, in DQN reduces its need for a separate target network, which is otherwise necessary to stabilize learning. We empirically show that, in the absence of a target network, the combination of Mellowmax and DQN outperforms DQN alone.

TIST Journal 2010 Journal Article

Planning interventions in biological networks

• Daniel Bryce
• Michael Verdicchio
• Seungchan Kim

Modeling the dynamics of biological processes has recently become an important research topic in computational biology and systems engineering. One of the most important reasons to model a biological process is to enable high-throughput in-silico experiments that attempt to predict or intervene in the process. These experiments can help accelerate the design of therapies through their rapid and inexpensive replication and alteration. While some techniques exist for reasoning with biological processes, few take advantage of the flexible and scalable algorithms popular in AI research. In reasoning about interventions in biological processes, where scalability is crucial for feasible application, we apply AI planning-based search techniques and demonstrate their advantage over existing enumerative methods. We also present a novel formulation of intervention planning that relies on models that characterize and attempt to change the phenotype of a system. We study three biological systems: the yeast cell cycle, a model of the human aging process, and the Wnt5a network governing the metastasis of melanoma in humans. The contribution of our investigation is in demonstrating that: (i) prior approaches, based on dynamic programming, cannot scale as well as heuristic search, and (ii) the newly found scalability enables us to plan previously unknown sequences of interventions that reveal novel and biologically significant responses in the systems which are consistent with biological knowledge in the literature.

IJCAI Conference 2007 Conference Paper

• Daniel Bryce
• Seungchan Kim

Modeling the dynamics of cellular processes has recently become a important research area of many disciplines. One of the most important reasons to model a cellular process is to enable high-throughput in-silico experiments that attempt to predict or intervene in the process. These experiments can help accelerate the design of therapies through their cheap replication and alteration. While some techniques exist for reasoning with cellular processes, few take advantage of the flexible and scalable algorithms popularized in AI research. In this domain, where scalability is crucial for feasible application, we apply AI planning based search techniques and demonstrate their advantage over existing enumerative methods.