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arXiv 2025年

作者： Kee, Hogun Oh, Wooseok Kang, Minjae Ahn, Hyemin Oh, Songhwai Department of Electrical and Computer Engineering ASRI Seoul National University Seoul08826 Korea Republic of Ulsan Korea Republic of

In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and (2) the difficulty of specifying the goal configuration of unseen objects. We address the former by presenting the tabletop tidying up (TTU) dataset, a structured dataset collected in simulation. Using this dataset, we train a vision-based discriminator capable of predicting the tidiness score. This discriminator can consistently evaluate the degree of tidiness across unseen configurations, including real-world scenes. Addressing the second problem, we employ Monte Carlo tree search (MCTS) to find tidying trajectories without specifying explicit goals. Instead of providing specific goals, we demonstrate that our MCTS-based planner can find diverse tidied configurations using the tidiness score as a guidance. Consequently, we propose TSMCTS, which integrates a tidiness discriminator with an MCTS-based tidying planner to find optimal tidied arrangements. TSMCTS has successfully demonstrated its capability across various environments, including coffee tables, dining tables, office desks, and bathrooms. The TTU dataset is available at: https://***/rllab-snu/ TTU-Dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Gradual Receptive Expansion Using Vision Transformer for Online 3D Bin Packing

Gradual Receptive Expansion Using Vision Transformer for Onl...

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

作者： Minjae Kang Hogun Kee Yoseph Park Junseok Kim Jaeyeon Jeong Geunje Cheon Jaewon Lee Songhwai Oh Department of Electrical and Computer Engineering and ASRI Seoul National University Seoul Korea Graduate School of Artificial Intelligence (GSAI) and ASRI Seoul National University Seoul Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The bin packing problem (BPP) is a challenging combinatorial optimization problem with a number of practical applications. This paper focuses on online 3D-BPP, where the packer makes immediate decisions for a loading position as items continually arrive. We propose a novel reinforcement learning algorithm, GREViT, which utilizes a vision transformer to tackle online 3D-BPP for the first time. By introducing the gradual receptive expansion technique, GREViT overcomes the limitations inherent in learning-based methods that only excel in their trained bins. As a result, GREViT surpasses existing BPP algorithms in packing ratio across various bin sizes. The effectiveness of GREViT in real-world scenarios is validated by its successful demonstrations using a real robot for solving 3D-BPP. The attached video demonstrates GREViT undertaking 3D-BPP in both simulated and real-world environments.

关键词： Training Learning systems computer vision Three-dimensional displays Loading Reinforcement learning Transformers Data augmentation Optimization Intelligent robots

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Exponential Synchronization of Heterogeneous Multi-Agent Systems via Linear-Signum-Type Diffusive Coupling: Blended Dynamics Approach

Exponential Synchronization of Heterogeneous Multi-Agent Sys...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Jeong Mo Seong Donggil Lee Hyungbo Shim Department of Electrical and Computer Engineering ASRI Seoul National University Seoul Republic of Korea

This paper investigates the behavior of heterogeneous agents that interact through diffusive coupling, resulting in emergent blended dynamics that may not be observed in the dynamics of individual agents. In particular, we propose a linear-signum-type diffusive coupling to achieve exponential synchronization among heterogeneous agents. This finding offers a notable advantage over previous research, which primarily focused on linear-type diffusive coupling with remaining steady-state error in synchronization. We provide conditions of both linear and signum coupling strengths for achieving the exponential synchronization and analyze the associated convergence rate. Furthermore, simulation results validate the effectiveness of the proposed approach.

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ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS 12

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

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Distributionally Robust Optimization with Unscented Transform for Learning-Based Motion Control in Dynamic Environments

Distributionally Robust Optimization with Unscented Transfor...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Astghik Hakobyan Insoon Yang Department of Electrical and Computer Engineering ASRI Seoul National University Seoul South Korea

Safety is one of the main challenges when applying learning-based motion controllers to practical robotic systems, especially when the dynamics of the robots and their surrounding dynamic environments are unknown. This issue is further exacerbated when the learned information is unreliable and inaccurate. In this paper, we aim to enhance the safety of learning-enabled mobile robots in dynamic environments from the perspective of distributionally robust optimization (DRO) and the unscented transform (UT). Our method infers the unknown dynamics of both the robot and the environment by adopting Gaussian process regression with an uncertainty propagation scheme based on UT to improve prediction accuracy. This leads to a novel learning-based model predictive control (MPC) method in which state information about both the robot and the environment is propagated via UT. The proposed method uses DRO to proactively limit the risk of collisions or other unsafe events in the presence of learning errors. However, the distributionally robust risk constraint is intractable because it involves a separate infinite-dimensional optimization problem. To overcome this challenge, we exploit UT with modern DRO techniques to replace the risk constraint with its simple upper bound. The performance and the utility of our method are demonstrated through simulations in autonomous driving scenarios, showing its capability to enhance safety and computational efficiency.

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Robust Model Reference Gaussian Process Regression: Enhancing Adaptability through Domain Randomization

Robust Model Reference Gaussian Process Regression: Enhancin...

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IEEE Conference on Decision and Control

作者： Hyuntae Kim Department of Electrical and Computer Engineering ASRI Seoul National University 1 Gwanak-ro Gwanak-gu Seoul Korea

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Nonlinear data-driven control strategies, particularly Model Reference Gaussian Process Regression (MRGPR), have been effective in designing controllers directly from system input/output data, bypassing the need for explicit system modeling. This approach is advantageous for complex nonlinear systems where traditional modeling methods may be inadequate. MR-GPR employs Gaussian Process Regression to provide a non-parametric control method, enhancing adaptability and performance. However, real-world applications present challenges due to variability in system parameters, such as ensuring robustness and consistent performance. To address these challenges, this paper proposes a robust MR-GPR controller incorporating domain randomization to improve adaptability to varying operational conditions. This extension aims to maintain stable performance across diverse settings, mitigating the impact of parameter changes on control efficacy.

关键词： Training Adaptation models Green products Gaussian processes Systems modeling Robustness Trajectory System identification Nonlinear systems Thermal stability

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Learning With Errors based Dynamic Encryption that Discloses Residue Signal for Anomaly Detection

arXiv

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arXiv 2024年

作者： Jang, Yeongjun Lee, Joowon Kim, Junsoo Shim, Hyungbo ASRI Department of Electrical and Computer Engineering Seoul National University Korea Republic of The Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea Republic of

Anomaly detection is a protocol that detects integrity attacks on control systems by comparing the residue signal with a threshold. Implementing anomaly detection on encrypted control systems has been a challenge because it is hard to detect an anomaly from the encrypted residue signal without the secret key. In this paper, we propose a dynamic encryption scheme for a linear system that automatically discloses the residue signal. The initial state and the input are encrypted based on the zero-dynamics of the system, so that the effect of encryption on the residue signal remains identically zero. The proposed scheme is shown to be secure in the sense that no other information than the residue signal is disclosed. Furthermore, we demonstrate a method of utilizing the disclosed residue signal to operate an observer-based controller over encrypted data for an infinite time horizon without re-encryption. © 2024, CC BY.

关键词： Control systems

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Data-Driven Inverse of Linear Systems and Application to Disturbance Observers

Data-Driven Inverse of Linear Systems and Application to Dis...

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American Control Conference (ACC)

作者： Yongsoon Eun Jaeho Lee Hyungbo Shim Department of Electrical Engineering and Computer Science DGIST Daegu South Korea Department of Electrical and Computer Engineering ASRI Seoul National University Seoul Korea

This work develops a data-based construction of inverse dynamics for LTI systems. Specifically, the problem addressed here is to find an input sequence from the corresponding output sequence based on pre-collected input and output data. The problem can be considered as a reverse of the recent use of the behavioral approach, in which the output sequence is obtained for a given input sequence by solving an equation formed by pre-collected data. The condition under which the problem gives a solution is investigated and turns out to be L-delay invertibility of the plant and a certain degree of persistent excitation of the data input. The result is applied to form a data-driven disturbance observer. The plant dynamics augmented by the data-driven disturbance observer exhibits disturbance rejection without the model knowledge of the plant.

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Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller for Strongly Controllable Systems

Model Reference Gaussian Process Regression: Data-Driven Sta...

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IEEE Conference on Decision and Control

作者： Hyuntae Kim ASRI Department of Electrical and Computer Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul Korea

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Data-driven control methods are gaining importance in control engineering, particularly for nonlinear systems where traditional models fall short. Many approaches rely on predefined libraries of functions, such as polynomials, to approximate system dynamics. These methods assume the selected functions can effectively represent the underlying system, but this assumption may not always hold, potentially impacting the robustness of control designs. The Model Reference Gaussian Process Regression (MR-GPR) controller was introduced to address these issues by using Gaussian Process Regression (GPR) with flexible kernels that adapt to observed data, eliminating the need for predefined function libraries. In this paper, we extend the MR-GPR controller to handle n-step controllable systems, allowing for more complex multi-step control tasks. We propose a state feedback control strategy based on realtime system data. Theoretical analysis confirms the stability of the closed-loop system under the MR-GPR controller, and numerical simulations validate its effectiveness.

关键词： State feedback Adaptation models System dynamics Gaussian processes Numerical simulation Libraries Stability analysis Robustness Polynomials Trajectory

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STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORKS 10

STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORK...

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10th International Conference on Learning Representations, ICLR 2022

作者： Hwang, Uiwon Kim, Heeseung Jung, Dahuin Jang, Hyemi Lee, Hyungyu Yoon, Sungroh Department of Electrical and Computer Engineering AIIS ASRI INMC ISRC NSI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in their latent spaces properly because they assume uniform distributions of the attributes. To address this problem, we theoretically derive Stein latent optimization that provides reparameterizable gradient estimations of the latent distribution parameters assuming a Gaussian mixture prior in a continuous latent space. Structurally, we introduce an encoder network and novel unsupervised conditional contrastive loss to ensure that data generated from a single mixture component represent a single attribute. We confirm that the proposed method, named Stein Latent Optimization for GANs (SLOGAN), successfully learns balanced or imbalanced attributes and achieves state-of-the-art unsupervised conditional generation performance even in the absence of attribute information (e.g., the imbalance ratio). Moreover, we demonstrate that the attributes to be learned can be manipulated using a small amount of probe data. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Generative adversarial networks

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