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6th Annual Learning for Dynamics and Control Conference

作者： Shin, Jaeuk Kim, Giho Lee, Howon Han, Joonho Yang, Insoon Seoul Natl Univ Dept Elect & Comp Engn ASRI Seoul South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence ASRI Seoul South Korea

Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data usage remains a central challenge to be tackled for its successful real-world applications. In this paper, we propose a sample-efficient meta-RL algorithm that learns a model of the system or environment at hand in a task-directed manner. As opposed to the standard model-based approaches to meta-RL, our method exploits the value information in order to rapidly capture the decision-critical part of the environment. The key component of our method is the loss function for learning both the task inference module and the system model. This systematically couples the model discrepancy and the value estimate, thereby enabling our proposed algorithm to learn the policy and task inference module with a significantly smaller amount of data compared to the existing meta-RL algorithms. The proposed method is evaluated in high-dimensional robotic control, empirically verifying its effectiveness in extracting information indispensable for solving the tasks from observations in a sample-efficient manner.

关键词： Reinforcement learning meta-reinforcement learning

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Regularizing Hard Examples Improves Adversarial Robustness

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JOURNAL OF MACHINE LEARNING RESEARCH 2025年 26卷

作者： Lee, Hyungyu Lee, Saehyung Bae, Ho Yoon, Sungroh Seoul Natl Univ Elect & Comp Engn Interdisciplinary Program Artificial Intelligence Seoul 08826 South Korea Ewha Womans Univ Dept Cyber Secur Seoul 03760 South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Elect & Comp Engn AIISASRIINMC Seoul 08826 South Korea Seoul Natl Univ ISRC Seoul 08826 South Korea

Recent studies have validated that pruning hard-to-learn examples from training improves the generalization performance of neural networks (NNs). In this study, we investigate this intriguing phenomenon-the negative effect of hard examples on generalization-in adversarial training. Particularly, we theoretically demonstrate that the increase in the difficulty of hard examples in adversarial training is significantly greater than the increase in the difficulty of easy examples. Furthermore, we verify that hard examples are only fitted through memorization of the label in adversarial training. We conduct both theoretical and empirical analyses of this memorization phenomenon, showing that pruning hard examples in adversarial training can enhance the model's robustness. However, the challenge remains in finding the optimal threshold for removing hard examples that degrade robustness performance. Based upon these observations, we propose a new approach, difficulty proportional label smoothing (DPLS), to adaptively mitigate the negative effect of hard examples, thereby improving the adversarial robustness of NNs. Notably, our experimental result indicates that our method can successfully leverage hard examples while circumventing the negative effect.

关键词： adversarial robustness adversarial examples hard examples memorization robust overfitting

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Adversarial Environment Design via Regret-Guided Diffusion Models 38

Adversarial Environment Design via Regret-Guided Diffusion M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chung, Hojun Lee, Junseo Kim, Minsoo Kim, Dohyeong Oh, Songhwai Interdisciplinary Program in Artificial Intelligence ASRI Seoul National University Korea Republic of Department of Electrical and Computer Engineering 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 Neural information processing systems foundation. All rights reserved.

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RNR-Nav: A Real-World Visual Navigation System Using Renderable Neural Radiance Maps

RNR-Nav: A Real-World Visual Navigation System Using Rendera...

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2024 International Conference on Intelligent Robots and Systems

作者： Kim, Minsoo Kwon, Obin Jun, Howoong Oh, Songhwai Interdisciplinary Program Artificial Intelligence Seoul South Korea ASRI Seoul South Korea Seoul Natl Univ Dept Elect & Comp Engn ECE Seoul South Korea Sequor Robot Inc Seoul South Korea

ISBN: (纸本)9798350377712;9798350377705

We propose a novel visual localization and navigation framework for real-world environments directly integrating observed visual information into the bird-eye-view map. While the renderable neural radiance map (RNR-Map) [1] shows considerable promise in simulated settings, its deployment in real-world scenarios poses undiscovered challenges. RNR-Map utilizes projections of multiple vectors into a single latent code, resulting in information loss under suboptimal conditions. To address such issues, our enhanced RNR-Map for real-world robots, RNR-Map++, incorporates strategies to mitigate information loss, such as a weighted map and positional encoding. For robust real-time localization, we integrate a particle filter into the correlation-based localization framework using RNR-Map++ without a rendering procedure. Consequently, we establish a real-world robot system for visual navigation utilizing RNR-Map++, which we call "RNR-Nav." Experimental results demonstrate that the proposed methods significantly enhance rendering quality and localization robustness compared to previous approaches. In real-world navigation tasks, RNR-Nav achieves a success rate of 84.4%, marking a 68.8% enhancement over the methods of the original RNR-Map paper.

关键词： Rendering (computer graphics)

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Safe Receding Horizon Motion Planning with Infinitesimal Update Interval

Safe Receding Horizon Motion Planning with Infinitesimal Upd...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jang, Inkyu Hwang, Sunwoo Byun, Jeonghyun Kim, H. Jin Seoul Natl Univ Dept Aerosp Engn Seoul South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350384581;9798350384574

Safety verification in motion planning is known to be computationally burdensome, despite its importance in robotics. In this paper, we investigate the behavior of safe receding horizon motion planners when the update interval becomes infinitesimal. By requiring the trajectory parameters to evolve continuously in time, the trajectory optimization problem is reformulated into a time-derivative form, whose decision variables are their rate of change. This results in a quadratic programming problem which directly provides safe input, and can be regarded as a real-time safety filter. The input expressivity is also enhanced by leveraging the differentiable structure of the parameter space. The proposed safety filter is experimentally validated using a wheeled ground robot in obstacle-cluttered environments. The result shows that the safety filter is capable of generating safe inputs in real-time, while addressing hundreds of constraints simultaneously.

关键词： Motion planning

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Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards

Safe CoR: A Dual-Expert Approach to Integrating Imitation Le...

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2024 International Conference on Intelligent Robots and Systems

作者： Kwon, Hyeokjin Lee, Gunmin Lee, Junseo Oh, Songhwai Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul 08826 South Korea Seoul Natl Univ ASRI Seoul 08826 South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea

ISBN: (纸本)9798350377712;9798350377705

In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges in navigating intricate environments such as complex driving situations. To overcome these challenges, we present the safe constraint reward (Safe CoR) framework, a novel method that utilizes two types of expert demonstrations-reward expert demonstrations focusing on performance optimization and safe expert demonstrations prioritizing safety. By exploiting a constraint reward (CoR), our framework guides the agent to balance performance goals of reward sum with safety constraints. We test the proposed framework in diverse environments, including the safety gym, metadrive, and the real-world Jackal platform. Our proposed framework improves algorithm performance by 39% and reduces constraint violations by 88% on the real-world Jackal platform, highlighting its effectiveness. Through this innovative approach, we expect significant advancements in real-world performance, leading to transformative effects in the realm of safe and reliable autonomous agents.

关键词： Adversarial machine learning

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Grokfast: Accelerated Grokking by Amplifying Slow Gradients

arXiv

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arXiv 2024年

作者： Lee, Jaerin Kang, Bong Gyun Kim, Kihoon Lee, Kyoung Mu ASRI Department of ECE Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

One puzzling artifact in machine learning dubbed grokking is where delayed generalization is achieved tenfolds of iterations after near perfect overfitting to the training data. Focusing on the long delay itself on behalf of machine learning practitioners, our goal is to accelerate generalization of a model under grokking phenomenon. By regarding a series of gradients of a parameter over training iterations as a random signal over time, we can spectrally decompose the parameter trajectories under gradient descent into two components: the fast-varying, overfitting-yielding component and the slow-varying, generalization-inducing component. This analysis allows us to accelerate the grokking phenomenon more than ×50 with only a few lines of code that amplifies the slow-varying components of gradients. The experiments show that our algorithm applies to diverse tasks involving images, languages, and graphs, enabling practical availability of this peculiar artifact of sudden generalization. Our code is available at https://***/ironjr/grokfast. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges

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artificial intelligence REVIEW 2025年第7期58卷 1-95页

作者： Kim, Jongseon Kim, Hyungjoon Kim, Hyungi Lee, Dongjun Yoon, Sungroh Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ AIIS ASRI Seoul South Korea Seoul Natl Univ INMC Seoul South Korea R&D Dept LG Chem Seoul South Korea Samsung SDI R&D Dept Yongin South Korea

Time series forecasting is a critical task that provides key information for decision-making across various fields, such as economic planning, supply chain management, and medical diagnosis. After the use of traditional statistical methodologies and machine learning in the past, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures, ranging from fundamental deep learning models to emerging architectures and hybrid approaches. In this context of exploration into various models, the architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining various deep learning models, we uncover new perspectives and present the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. This survey explores vital elements that can enhance forecasting performance through diverse approaches. These contributions help lower entry barriers for newcomers by providing a systematic underst

关键词： Time series forecasting Deep learning Foundation model Distribution shift Causality

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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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Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards

Safe CoR: A Dual-Expert Approach to Integrating Imitation Le...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hyeokjin Kwon Gunmin Lee Junseo Lee Songhwai Oh Interdisciplinary Program in Artificial Intelligence and ASRI Seoul National University Seoul Korea Department of Electrical and Computer Engineering and ASRI Seoul National University Seoul Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges in navigating intricate environments such as complex driving situations. To overcome these challenges, we present the safe constraint reward (Safe CoR) framework, a novel method that utilizes two types of expert demonstrations—reward expert demonstrations focusing on performance optimization and safe expert demonstrations prioritizing safety. By exploiting a constraint reward (CoR), our framework guides the agent to balance performance goals of reward sum with safety constraints. We test the proposed framework in diverse environments, including the safety gym, metadrive, and the real-world Jackal platform. Our proposed framework improves algorithm performance by 39% and reduces constraint violations by 88% on the real-world Jackal platform, highlighting its effectiveness. Through this innovative approach, we expect significant advancements in real-world performance, leading to transformative effects in the realm of safe and reliable autonomous agents.

关键词： Navigation Imitation learning Focusing Reinforcement learning Autonomous agents Safety Reliability Optimization Intelligent robots Faces

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