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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Lee, Seungbeom Kim, Dongwoo Chung, Heewon Kim, Junsoo Shim, Hyungbo Seoul National University Asri Department of Electrical and Computer Engineering Korea Republic of Dongguk University Department of Ai · Sw Convergence Korea Republic of Desilo Inc Korea Republic of Seoul National University of Science and Technology Department of Electrical and Information Engineering Korea Republic of

ISBN: (纸本)9798350316339

We propose an authentication scheme for a multiagent system over integers, based on verifiable computation primitives. The naive approach, employing Freivalds' algorithm in centralized way, faces several challenges. Specifically, unreliability of the network introduces the risk of information tampering by other agents. To this end, it requires locally updating and merging the proofs of the individual states in a distributed manner. Our proposed method addresses these issues with assuming presence of a leader agent who is responsible for validating the correctness of all the states of the agents. This can be achieved by a distributed protocol that aggregates proofs from the individual agents, relying on the well-known knowledge-of-exponent assumption. By using this distributed protocol, computational complexity and communication cost are reduced compared to centralized verification. Furthermore, we incorporate a clustering technique to distribute storage requirements. © 2024 IEEE.

关键词： Authentication Protocol

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Reference-Tracking Sliding-Mode Control Robust to Mismatched Exogenous Disturbances

Reference-Tracking Sliding-Mode Control Robust to Mismatched...

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Asian Control Conference

作者： Sunghyun Koo Hyungbo Shim ASRI Department of Electrical and Computer Engineering Seoul National University Seoul Korea

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

A dynamic state-feedback sliding-mode controller (SMC) based on the internal model principle (IMP) is proposed for the tracking of exogenous signals. The proposed SMC is robust to both matched disturbances and mismatched exogenous disturbances. It also achieves robustness to matched parametric uncertainties and a certain degree of mismatched parametric uncertainties. The stability of the closed-loop system and the existence of a global solution are theoretically guaranteed by a specific initialization of the internal model. Its performance is demonstrated through numerical simulations.

关键词： Uncertainty Numerical simulation Stability analysis Robustness Regulation Numerical models Closed loop systems

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TOWARDS REALISTIC INCREMENTAL SCENARIO IN CLASS INCREMENTAL SEMANTIC SEGMENTATION 3

TOWARDS REALISTIC INCREMENTAL SCENARIO IN CLASS INCREMENTAL ...

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3rd Conference on Lifelong Learning Agents, CoLLAs 2024

作者： Kwak, Jihwan Cha, Sungmin Moon, Taesup Department of Electrical and Computer Engineering Seoul National University Korea Republic of Computer Science Department Courant Institute of Mathematical Sciences New York University United States ASRI INMC IPAI AIIS Seoul National University Korea Republic of

This paper addresses the unrealistic aspect of the overlapped scenario, a commonly adopted incremental learning scenario in Class Incremental Semantic Segmentation (CISS). We highlight that the overlapped scenario allows the same image to reappear in future tasks with different pixel labels, creating unwanted advantage or disadvantage to widely used techniques in CISS, such as pseudo-labeling and data replay from the exemplar memory. Our experiments show that methods trained and evaluated under the overlapped scenario can produce biased results, potentially affecting algorithm adoption in practical applications. To mitigate this, we propose a practical scenario called partitioned, where the dataset is first divided into distinct subsets representing each class, and then these subsets are assigned to corresponding tasks. This efficiently addresses the data reappearance artifact while meeting other requirements of CISS scenario, such as capturing the background shifts. Additionally, we identify and resolve the code implementation issues related to replaying data from the exemplar memory, previously overlooked in other works. Lastly, we introduce a simple yet competitive memory-based baseline, MiB-AugM, that handles background shifts in the exemplar memory. This baseline achieves state-of-the-art results across multiple tasks involving learning many new classes. Codes are available at https://***/jihwankwak/CISS_partitioned. © 2024 CoLLAs. All Rights Reserved.

关键词： Semantic Segmentation

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Renderable Neural Radiance Map for Visual Navigation

Renderable Neural Radiance Map for Visual Navigation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Obin Kwon Jeongho Park Songhwai Oh Department of Electrical and Computer Engineering ASRI Seoul National University

We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. The RNR-Map has a grid form and consists of latent codes at each pixel. These latent codes are embedded from image observations, and can be converted to the neural radiance field which enables image rendering given a camera pose. The recorded latent codes implicitly contain visual information about the environment, which makes the RNR-Map visually descriptive. This visual information in RNR-Map can be a useful guideline for visual localization and navigation. We develop localization and navigation frameworks that can effectively utilize the RNR-Map. We evaluate the proposed frameworks on camera tracking, visual localization, and image-goal navigation. Experimental results show that the RNR-Map-based localization framework can find the target location based on a single query image with fast speed and competitive accuracy compared to other baselines. Also, this localization framework is robust to environmental changes, and even finds the most visually similar places when a query image from a different environment is given. The proposed navigation framework outperforms the existing image-goal navigation methods in difficult scenarios, under odometry and actuation noises. The navigation framework shows 65.7% success rate in curved scenarios of the NRNS [21] dataset, which is an improvement of 18.6% over the current state-of-the-art. Project page: https://***/projects/RNR-Map/

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Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments

arXiv

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arXiv 2025年

作者： Shin, Jaeuk Lee, Jungjin Yang, Insoon Department of Electrical and Computer Engineering ASRI Seoul National University Seoul08826 Korea Republic of

Conformal prediction (CP) has emerged as a powerful tool in robotics and control, thanks to its ability to calibrate complex, data-driven models with formal guarantees. However, in robot navigation tasks, existing CP-based methods often decouple prediction from control, evaluating models without considering whether prediction errors actually compromise safety. Consequently, ego-vehicles may become overly conservative or even immobilized when all potential trajectories appear infeasible. To address this issue, we propose a novel CP-based navigation framework that responds exclusively to safety-critical prediction errors. Our approach introduces egocentric score functions that quantify how much closer obstacles are to a candidate vehicle position than anticipated. These score functions are then integrated into a model predictive control scheme, wherein each candidate state is individually evaluated for safety. Combined with an adaptive CP mechanism, our framework dynamically adjusts to changes in obstacle motion without resorting to unnecessary conservatism. Theoretical analyses indicate that our method outperforms existing CP-based approaches in terms of cost-efficiency while maintaining the desired safety levels, as further validated through experiments on real-world datasets featuring densely populated pedestrian environments. © 2025, CC BY-NC-ND.

关键词： Prediction models

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Disturbance Observer with Switched Output Redefinition for Robust Stabilization of Non-Minimum Phase Linear Systems 63

Disturbance Observer with Switched Output Redefinition for R...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Chang, Hamin Song, Donghyeon Trenn, Stephan Shim, Hyungbo Seoul National University Asri Department of Electrical and Computer Engineering 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Bernoulli Institute University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands

ISBN: (纸本)9798350316339

Q-filter-based disturbance observer (DOB) has emerged as a powerful robust control technique renowned for its effectiveness in mitigating disturbances and addressing plant uncertainties. Despite its advantage, a key limitation of the Q-filter-based DOB lies in its requirement for plants to be of minimum phase. In this paper, we introduce an approach allowing the utilization of the Q-filter-based DOB as a stabilizing controller for non-minimum phase linear systems based on switched output redefinition of the systems. By redefining the output of systems to be controlled periodically, the approach stabilizes unstable internal dynamics of the systems as well as the original output. The proposed method is verified by an illustrative example. © 2024 IEEE.

关键词： Robust control

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User-Friendly Vehicle-to-Grid Optimal Scheduling Problem and Distributed Implementation for Plug-and-Play Operation 23

User-Friendly Vehicle-to-Grid Optimal Scheduling Problem and...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Lee, Seungbeom Heo, Jinwook Hyeon, Soojeong Nam, Jiyeon Shim, Hyungbo Kim, Jinsung Asri Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Hyundai Motor Company Vehicle Sw Division Hwaseong18280 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose an optimization method of charging scheduling algorithm based on preference of vehicle owners. To suffice various desires for vehicle charging, we design objective function of optimization problem as weighted sum of some objective function to reflect various desires. To solve the problem without aggregating weight coefficient, distributed optimization based on dynamic average consensus method is implemented. By initialization-free property of dynamic average consensus, plug-and-play operation of electric vehicle is applicable. Finally, we simulate the proposed algorithm to enlighten the meaning of weighted sum for objective function and plug-and-play scenario. © 2023 ICROS.

关键词： Vehicle-to-grid

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RE-WEIGHTING BASED GROUP FAIRNESS REGULARIZATION VIA CLASSWISE ROBUST OPTIMIZATION 11

RE-WEIGHTING BASED GROUP FAIRNESS REGULARIZATION VIA CLASSWI...

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11th International Conference on Learning Representations, ICLR 2023

作者： Jung, Sangwon Park, Taeeon Chun, Sanghyuk Moon, Taesup Department of Electrical and Computer Engineering Seoul National University Korea Republic of NAVER AI Lab United States ASRI INMC IPAI AIIS Seoul National University Korea Republic of

Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as FairDRO, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a classwise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Economic and social effects

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Singularly Perturbed Hybrid Systems for Analysis of Networks with Frequently Switching Graphs

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IEEE Transactions on Automatic Control 2024年第7期70卷 4344-4359页

作者： Tanwani, Aneel Shim, Hyungbo Teel, Andrew R. University of Toulouse CNRS LAAS France Seoul National University ASRI Department of Electrical and Computer Engineering Korea Republic of University of California Department of Electrical and Computer Engineering Santa Barbara United States

For a class of hybrid systems, where jumps occur frequently, we analyze the stability of system trajectories in view of singularly perturbed dynamics. The specific model we consider comprises an interconnection of two hybrid subsystems, a timer which triggers the jumps, and some discrete variables to determine the index of the jump maps. The flow equations of these variables are singularly perturbed differential equations and, in particular, a smaller value of the singular perturbation parameter leads to an increase in the frequency of the jump instants. For the limiting value of this parameter, we consider a decomposition which comprises a quasi-steady-state system modeled by a differential equation without any jumps and a boundary-layer system described by purely discrete dynamics. Under appropriate assumptions on the quasi-steady-state system and the boundary-layer system, we derive results showing practical stability of a compact attractor when the jumps occur sufficiently often. As an application of our results, we discuss the control design problem in a network of second-order continuous-time coupled oscillators, where each agent communicates the information about its position to some of its neighbors at discrete times. Using the results developed in this article, we show that if the union of the communication graphs being used for information exchange between agents is connected, then the oscillators achieve practical consensus. © 1963-2012 IEEE.

关键词： Continuous time systems

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Adversarial Environment Design via Regret-Guided Diffusion Models

arXiv

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arXiv 2024年

作者： Chung, Hojun Lee, Junseo Kim, Minsoo Kim, Dohyeong Oh, Songhwai Interdisciplinary Program in Artificial Intelligence and ASRI Seoul National University Korea Republic of Department of Electrical and Computer Engineering and ASRI Seoul National University Korea Republic of

Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generating a set of training environments tailored to the agent’s capabilities. While prior works demonstrate that UED has the potential to learn a robust policy, their performance is constrained by the capabilities of the environment generation. To this end, we propose a novel UED algorithm, adversarial environment design via regret-guided diffusion models (ADD). The proposed method guides the diffusion-based environment generator with the regret of the agent to produce environments that the agent finds challenging but conducive to further improvement. By exploiting the representation power of diffusion models, ADD can directly generate adversarial environments while maintaining the diversity of training environments, enabling the agent to effectively learn a robust policy. Our experimental results demonstrate that the proposed method successfully generates an instructive curriculum of environments, outperforming UED baselines in zero-shot generalization across novel, out-of-distribution environments. Project page: https://***/projects/ADD © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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