About

Byung-Cheol (B.C.) Min is a Professor in the Department of Computer Science and the Department of Intelligent Systems Engineering at Indiana University Bloomington, a position he assumed in Fall 2025. He also serves as an Adjunct Professor in the School of Applied and Creative Computing at Purdue University. For the most current information about his work and academic activities, Professor Min directs visitors to his new webpage and the SMART Lab's website, which is his research laboratory. The provided text does not include specific details about his research focus, background, or key contributions beyond his academic appointments and affiliations.

Research topics

• Computer Science
• Engineering
• Computer Security
• Political Science
• Artificial Intelligence
• Systems engineering
• Operations research
• Risk analysis (engineering)
• Simulation
• Software engineering
• Operations management
• Human–computer interaction
• Database
• Business
• Law

Selected publications

• PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning

  arXiv (Cornell University) · 2026-05-01

  preprintOpen accessSenior author

  Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedback. However, large-scale preference datasets, whether collected from crowdsourced annotators or generated by synthetic teachers, often contain heterogeneous and partially conflicting supervision, including disagreement across annotators and inconsistency within annotators. Existing reward learning methods typically fit a single reward model to such data, forcing it to average incompatible signals and thereby limiting robustness. To solve this, we propose PrefMoE, a mixture-of-experts reward learning framework for robust preference modeling. PrefMoE learns multiple specialized reward experts and uses trajectory-level soft routing to combine them adaptively, enabling the model to capture diverse latent preference patterns under noisy and heterogeneous preference supervision. A load-balancing regularizer further stabilizes training by preventing expert collapse. Across locomotion benchmarks from D4RL and manipulation tasks from MetaWorld, PrefMoE improves preference prediction robustness and leads to more reliable downstream policy learning than strong single-model baselines.

  PublisherDOI

• Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems

  arXiv (Cornell University) · 2026-04-07

  preprintOpen accessSenior author

  Large Language Models (LLMs) are increasingly used to convert task commands into robot-executable code, however this pipeline lacks validation gates to detect unsafe and defective commands before they are translated into robot code. Furthermore, even commands that appear safe at the outset can produce unsafe state transitions during execution in the absence of continuous constraint monitoring. In this research, we introduce SafeGate, a neurosymbolic safety architecture that prevents unsafe natural language task commands from reaching robot execution. Drawing from ISO 13482 safety standard, SafeGate extracts structured safety-relevant properties from natural language commands and applies a deterministic decision gate to authorize or reject execution. In addition, we introduce Task Safety Contracts, which decomposes commands that pass through the gate into invariants, guards, and abort conditions to prevent unsafe state transitions during execution. We further incorporate Z3 SMT solving to enforce constraint checking derived from the Task Safety Contracts. We evaluate SafeGate against existing LLM-based robot safety frameworks and baseline LLMs across 230 benchmark tasks, 30 AI2-THOR simulation scenarios, and real-world robot experiments. Results show that SafeGate significantly reduces the acceptance of defective commands while maintaining a high acceptance of benign tasks, demonstrating the importance of pre-execution safety gates for LLM-controlled robot systems

  PublisherDOI

• PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning

  arXiv (Cornell University) · 2026-05-01

  articleOpen accessSenior author

  Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedback. However, large-scale preference datasets, whether collected from crowdsourced annotators or generated by synthetic teachers, often contain heterogeneous and partially conflicting supervision, including disagreement across annotators and inconsistency within annotators. Existing reward learning methods typically fit a single reward model to such data, forcing it to average incompatible signals and thereby limiting robustness. To solve this, we propose PrefMoE, a mixture-of-experts reward learning framework for robust preference modeling. PrefMoE learns multiple specialized reward experts and uses trajectory-level soft routing to combine them adaptively, enabling the model to capture diverse latent preference patterns under noisy and heterogeneous preference supervision. A load-balancing regularizer further stabilizes training by preventing expert collapse. Across locomotion benchmarks from D4RL and manipulation tasks from MetaWorld, PrefMoE improves preference prediction robustness and leads to more reliable downstream policy learning than strong single-model baselines.

  PublisherOA PDF

• Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems

  arXiv (Cornell University) · 2026-04-07

  articleOpen accessSenior author

  Large Language Models (LLMs) are increasingly used to convert task commands into robot-executable code, however this pipeline lacks validation gates to detect unsafe and defective commands before they are translated into robot code. Furthermore, even commands that appear safe at the outset can produce unsafe state transitions during execution in the absence of continuous constraint monitoring. In this research, we introduce SafeGate, a neurosymbolic safety architecture that prevents unsafe natural language task commands from reaching robot execution. Drawing from ISO 13482 safety standard, SafeGate extracts structured safety-relevant properties from natural language commands and applies a deterministic decision gate to authorize or reject execution. In addition, we introduce Task Safety Contracts, which decomposes commands that pass through the gate into invariants, guards, and abort conditions to prevent unsafe state transitions during execution. We further incorporate Z3 SMT solving to enforce constraint checking derived from the Task Safety Contracts. We evaluate SafeGate against existing LLM-based robot safety frameworks and baseline LLMs across 230 benchmark tasks, 30 AI2-THOR simulation scenarios, and real-world robot experiments. Results show that SafeGate significantly reduces the acceptance of defective commands while maintaining a high acceptance of benign tasks, demonstrating the importance of pre-execution safety gates for LLM-controlled robot systems

  PublisherOA PDF

• Personalization in Human-Robot Interaction Through Preference-Based Action Representation Learning

  2025-05-19 · 3 citations

  articleSenior author

  Preference- based reinforcement learning (PbRL) has shown significant promise for personalization in human- robot interaction (HRI) by explicitly integrating human preferences into the robot learning process. However, existing practices often require training a personalized robot policy from scratch, resulting in inefficient use of human feedback. In this paper, we propose preference-based action representation learning (PbARL), an efficient fine-tuning method that decouples common task structure from preference by leveraging pre-trained robot policies. Instead of directly fine-tuning the pre-trained policy with human preference, PbARL uses it as a reference for an action representation learning task that maximizes the mutual information between the pre-trained source domain and the target user preference-aligned domain. This approach allows the robot to personalize its behaviors while preserving original task performance and eliminates the need for extensive prior information from the source domain, thereby enhancing efficiency and practicality in real-world HRI scenarios. Empirical results on the Assistive Gym benchmark and a real-world user study (N=8) demonstrate the benefits of our method compared to state-of-the-art approaches. Website at https://sites.google.com/view/pbarl.

  PublisherDOI

• Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation

  ArXiv.org · 2025-03-12 · 1 citations

  preprintOpen accessSenior author

  Recent advances in robotics and large language models (LLMs) have sparked growing interest in human-robot collaboration and embodied intelligence. To enable the broader deployment of robots in human-populated environments, socially-aware robot navigation (SAN) has become a key research area. While deep reinforcement learning approaches that integrate human-robot interaction (HRI) with path planning have demonstrated strong benchmark performance, they often struggle to adapt to new scenarios and environments. LLMs offer a promising avenue for zero-shot navigation through commonsense inference. However, most existing LLM-based frameworks rely on centralized decision-making, lack robust verification mechanisms, and face inconsistencies in translating macro-actions into precise low-level control signals. To address these challenges, we propose SAMALM, a decentralized multi-agent LLM actor-critic framework for multi-robot social navigation. In this framework, a set of parallel LLM actors, each reflecting distinct robot personalities or configurations, directly generate control signals. These actions undergo a two-tier verification process via a global critic that evaluates group-level behaviors and individual critics that assess each robot's context. An entropy-based score fusion mechanism further enhances self-verification and re-query, improving both robustness and coordination. Experimental results confirm that SAMALM effectively balances local autonomy with global oversight, yielding socially compliant behaviors and strong adaptability across diverse multi-robot scenarios. More details and videos about this work are available at: https://sites.google.com/view/SAMALM.

  PublisherOA PDFDOI

• Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems

  ArXiv.org · 2025-10-17

  preprintOpen accessSenior author

  In this paper, we propose a novel few-shot learning framework for multi-robot systems that integrate both spatial and temporal elements: Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution (DDACE). Our approach leverages temporal graph networks for learning task-agnostic temporal sequencing and Gaussian Processes for spatial trajectory modeling, ensuring modularity and generalization across various tasks. By decoupling temporal and spatial aspects, DDACE requires only a small number of demonstrations, significantly reducing data requirements compared to traditional learning from demonstration approaches. To validate our proposed framework, we conducted extensive experiments in task environments designed to assess various aspects of multi-robot coordination-such as multi-sequence execution, multi-action dynamics, complex trajectory generation, and heterogeneous configurations. The experimental results demonstrate that our approach successfully achieves task execution under few-shot learning conditions and generalizes effectively across dynamic and diverse settings. This work underscores the potential of modular architectures in enhancing the practicality and scalability of multi-robot systems in real-world applications. Additional materials are available at https://sites.google.com/view/ddace.

  PublisherOA PDFDOI

• ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models

  2025-05-19 · 2 citations

  articleSenior author

  Incorporating language comprehension into robotic operations unlocks significant advancements in robotics, but also presents distinct challenges, particularly in executing spatially oriented tasks like pattern formation. This paper introduces ZeroCAP, a novel system that integrates large language models with multi-robot systems for zero-shot context aware pattern formation. Grounded in the principles of language-conditioned robotics, ZeroCAP leverages the interpretative power of language models to translate natural language instructions into actionable robotic configurations. This approach combines the synergy of vision-language models, cutting-edge segmentation techniques and shape descriptors, enabling the realization of complex, context-driven pattern formations in the realm of multi robot coordination. Through extensive experiments, we demonstrate the systems proficiency in executing complex context aware pattern formations across a spectrum of tasks, from surrounding and caging objects to infilling regions. This not only validates the system's capability to interpret and implement intricate context-driven tasks but also underscores its adaptability and effectiveness across varied environments and scenarios. The experimental videos and additional information about this work can be found at https://sites.google.com/view/zerocap/home.

  PublisherDOI

• PrefCLM: Enhancing Preference-Based Reinforcement Learning With Crowdsourced Large Language Models

  IEEE Robotics and Automation Letters · 2025-01-15 · 9 citations

  articleSenior author

  Preference-based reinforcement learning (PbRL) is emerging as a promising approach to teaching robots through human comparative feedback without complex reward engineering. However, the substantial volume of human feedback required hinders broader applications. In this work, we introduce PrefCLM, a novel framework that utilizes crowdsourced large language models (LLMs) as synthetic teachers in PbRL. We utilize Dempster-Shafer Theory to fuse individual preference beliefs from multiple LLM agents at the score level, efficiently leveraging their diversity and collective intelligence. We also introduce a human-in-the-loop pipeline, enabling iterative and collective refinements that adapt to the nuanced and individualized preferences inherent to human-robot interaction (HRI) scenarios. Experimental results across various general RL tasks show that PrefCLM achieves competitive performance compared to expert-engineered scripted teachers and excels in facilitating more natural and efficient behaviors. A real-world user study (N = 10) further demonstrates its capability to tailor robot behaviors to individual user preferences, enhancing user satisfaction in HRI scenarios.

  PublisherDOI

• Multimodal Audio-Based Disease Prediction With Transformer-Based Hierarchical Fusion Network

  IEEE Transactions on Audio Speech and Language Processing · 2025-01-01 · 2 citations

  articleSenior author

  Audio-based disease prediction is emerging as a promising supplement to traditional medical diagnosis methods, facilitating early, convenient, and non-invasive disease detection and prevention. Multimodal fusion, which integrates features from various domains within or across bio-acoustic modalities, has proven effective in enhancing diagnostic performance. However, most existing methods in the field employ unilateral fusion strategies that focus solely on either intra-modal or inter-modal fusion. This approach limits the full exploitation of the complementary nature of diverse acoustic feature domains and bio-acoustic modalities. Additionally, the inadequate and isolated exploration of latent dependencies within modality-specific and modality-shared spaces curtails their capacity to manage the inherent heterogeneity in multimodal data. To fill these gaps, we propose a transformer-based hierarchical fusion network designed for general multimodal audio-based disease prediction. Specifically, we seamlessly integrate intra-modal and inter-modal fusion in a hierarchical manner and proficiently encode the necessary intra-modal and inter-modal complementary correlations, respectively. Comprehensive experiments demonstrate that our model achieves state-of-the-art performance in predicting three diseases: COVID-19, Parkinson's disease, and pathological dysarthria, showcasing its promising potential in a broad context of audio-based disease prediction tasks. Additionally, extensive ablation studies and qualitative analyses highlight the significant benefits of each main component within our model.

  PublisherDOI

Recent grants

• CAREER: Adaptive Human Multi-Robot Systems

  NSF · $500k · 2019–2025

Frequent coauthors

• Wonse Jo

  Purdue University West Lafayette

  39 shared

• Eric T. Matson

  Purdue University System

  24 shared

• Shyam Sundar Kannan

  24 shared

• Richard M. Voyles

  22 shared

• Tamzidul Mina

  Sandia National Laboratories

  21 shared

• Donghan Kim

  17 shared

• Ruiqi Wang

  Start Making A Reader Today

  14 shared

• Dezhong Zhao

  14 shared

Education

• Postdoc, Robotics Institute

  Carnegie Mellon University

  2015

• Ph.D., Computer and Information Technology

  Purdue University

  2014

Awards & honors

• NSF CAREER Award (2019)
• Purdue PPI Outstanding Faculty Award in Discovery (2019)
• Purdue CIT Outstanding Faculty Award in Discovery (2019)
• Purdue CIT Outstanding Graduate Mentor Award (2019)
• Purdue Focus Award (2020)