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Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding

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Meta-Explore: Exploratory Hierarchical Vision-and-Language N...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Minyoung Hwang Jaeyeon Jeong Minsoo Kim Yoonseon Oh Songhwai Oh Electrical and Computer Engineering and ASRI Seoul National University Interdisciplinary Major in Artificial Intelligence Seoul National University Department of Electronic Engineering Hanyang University

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outper-forms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1% and SPL by 20. 6% against the state-of-the-art method of the SOON benchmark. Project page: https://***/projects/Meta-Explore/***

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Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP

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arXiv 2025年

作者： Park, Junsung Lee, Jungbeom Song, Jongyoon Yu, Sangwon Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Amazon United States School of Computer Science and Engineering Soongsil University Korea Republic of IPAI AIIS ASRI INMC ISRC Seoul National University Korea Republic of

While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation—such as failing to differentiate concepts like "parking" from "no parking"—poses substantial challenges. By analyzing the data used in the public CLIP model’s pre-training, we posit this limitation stems from a lack of negation-inclusive data. To address this, we introduce data generation pipelines that employ a large language model (LLM) and a multimodal LLM to produce negation-inclusive captions. Fine-tuning CLIP with data generated from our pipelines, we develop NegationCLIP, which enhances negation awareness while preserving the generality. Moreover, to enable a comprehensive evaluation of negation understanding, we propose NegRefCOCOg—a benchmark tailored to test VLMs’ ability to interpret negation across diverse expressions and positions within a sentence. Experiments on various CLIP architectures validate the effectiveness of our data generation pipelines in enhancing CLIP’s ability to perceive negation accurately. Additionally, NegationCLIP’s enhanced negation awareness has practical applications across various multimodal tasks, demonstrated by performance gains in text-to-image generation and referring image segmentation. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

Design of Q-Filter-Based Disturbance Observer for Differential Algebraic Equations and a Robust Stability Condition: Zero Relative Degree Case

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Design of Q-Filter-Based Disturbance Observer for Differenti...

IEEE Conference on Decision and Control

作者： Hamin Chang Stephan Trenn Bernoulli Institute University of Groningen AG Groningen The Netherlands Department of Electrical and Computer Engineering ASRI Seoul National University Seoul Korea

While the disturbance observer (DOB)-based controller is widely utilized in various practical applications, there has been a lack of extension of its use to differential algebraic equations (DAEs). In this paper, we introduce several lemmas that establish necessary and/or sufficient conditions for specifying the relative degree of DAEs. Using these lemmas, we also figure out that there is a class of DAEs which can be viewed as linear systems with zero relative degree. For the class of DAEs, we propose a design of Q-filter-based DOB as well as a robust stability condition for systems controlled by the DOB through time domain analysis using singular perturbation theory. The proposed stability condition is verified by an illustrative example.

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VoiceGuider: Enhancing Out-of-Domain Performance in Parameter-Efficient Speaker-Adaptive Text-to-Speech via Autoguidance

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arXiv 2024年

作者： Yeom, Jiheum Kim, Heeseung Choi, Jooyoung Lee, Che Hyun Park, Nohil Yoon, Sungroh Electrical and Computer Engineering Seoul National University Seoul Korea Republic of ECE AIIS ASRI INMC ISRC IPAI Seoul National University Seoul Korea Republic of

When applying parameter-efficient finetuning via LoRA onto speaker adaptive text-to-speech models, adaptation performance may decline compared to full-finetuned counterparts, especially for out-of-domain speakers. Here, we propose VoiceGuider, a parameter-efficient speaker adaptive text-to-speech system reinforced with autoguidance to enhance the speaker adaptation performance, reducing the gap against full-finetuned models. We carefully explore various ways of strengthening autoguidance, ultimately finding the optimal strategy. VoiceGuider as a result shows robust adaptation performance especially on extreme out-of-domain speech data. We provide audible samples in our demo page. © 2024, CC BY.

关键词： Speech enhancement

Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller

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arXiv 2023年

作者： Kim, Hyuntae Chang, Hamin Shim, Hyungbo ASRI Department of Electrical and Computer Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system’s state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

Effective Exploration via Tsallis Actor-Critic on 6D Robot Grasping

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Effective Exploration via Tsallis Actor-Critic on 6D Robot G...

International Conference on Control, Automation and Systems ( ICCAS)

作者： Jaeyeon Jeong Jonghyun Park Songhwai Oh Department of Electrical and Computer Engineering Seoul National University Seoul Korea ASRI Seoul National University Seoul Korea Department of Naval Architecture and Ocean Engineering Seoul National University Seoul Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Grasping arbitrary objects is challenging due to the need to understand unseen object shapes, generate plausible grasp poses, and plan trajectories simultaneously. In this paper, we propose Goal-Auxiliary Tsallis Actor-Critic (GA-TAC) method, which enhances grasping policy learning by integrating goal prediction auxiliary tasks with Tsallis Actor-Critic [1] methods. Building on the Goal-Auxiliary Deep Deterministic Policy Gradient (GA-DDPG) [2], GA-TAC addresses the limitations of suboptimal grasp policies by dynamically adjusting the exploration-exploitation trade-off using the Tsallis Actor-Critic method. This hybrid approach leverages the strengths of both learning paradigms and adaptive exploration, resulting in more robust and optimal grasping policies. We demonstrate that GA-TAC significantly increases the success rate of grasping tasks by ensuring effective exploration near critical grasping points in Dex-YCB dataset [3] with different q-indices. We also show that GA-TAC outperforms other baselines in the Handover-Sim [4] environment and our method is robust to unseen environments.

关键词： Automation Shape Buildings Grasping Reinforcement learning Handover Control systems Trajectory Collision avoidance Robots

Sequential preference ranking for efficient reinforcement learning from human feedback 23

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Sequential preference ranking for efficient reinforcement le...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Minyoung Hwang Gunmin Lee Hogun Kee Chanwoo Kim Kyungjae Lee Songhwai Oh Electrical and Computer Engineering and ASRI Seoul National University Seoul Korea Department of Artificial Intelligence Chung-Ang University Seoul Korea

Reinforcement learning from human feedback (RLHF) alleviates the problem of designing a task-specific reward function in reinforcement learning by learning it from human preference. However, existing RLHF models are considered inefficient as they produce only a single preference data from each human feedback. To tackle this problem, we propose a novel RLHF framework called SeqRank, that uses sequential preference ranking to enhance the feedback efficiency. Our method samples trajectories in a sequential manner by iteratively selecting a defender from the set of previously chosen trajectories Κ and a challenger from the set of unchosen trajectories u \ Κ, where u is the replay buffer. We propose two trajectory comparison methods with different defender sampling strategies: (1) sequential pairwise comparison that selects the most recent trajectory and (2) root pairwise comparison that selects the most preferred trajectory from Κ. We construct a data structure and rank trajectories by preference to augment additional queries. The proposed method results in at least 39.2% higher average feedback efficiency than the baseline and also achieves a balance between feedback efficiency and data dependency. We examine the convergence of the empirical risk and the generalization bound of the reward model with Rademacher complexity. While both trajectory comparison methods outperform conventional pairwise comparison, root pairwise comparison improves the average reward in locomotion tasks and the average success rate in manipulation tasks by 29.0% and 25.0%, respectively. Project page: https://***/projects/SeqRank

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Authentication of Multi-agent System with Verifiable Computation and Distributed Aggregation

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Authentication of Multi-agent System with Verifiable Computa...

IEEE Conference on Decision and Control

作者： Seungbeom Lee Dongwoo Kim Heewon Chung Junsoo Kim Hyungbo Shim Department of Electrical and Computer Engineering ASRI Seoul National University Korea Department of AI · SW Convergence Dongguk University Korea DESILO Inc Korea Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

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.

关键词： Protocols Costs Data integrity Aggregates Merging Authentication Clustering algorithms Computational complexity Faces Multi-agent systems

Ring-LWE based Encrypted Controller with Unlimited Number of Recursive Multiplications and Effect of Error Growth

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arXiv 2024年

作者： Jang, Yeongjun Lee, Joowon Min, Seonhong Kwak, Hyesun Kim, Junsoo Song, Yongsoo ASRI The Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Information Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

In this paper, we propose an encrypted dynamic controller that executes an unlimited number of recursive homomorphic multiplications on a Ring Learning With Errors (Ring-LWE) based cryptosystem without bootstrapping. The proposed controller exhibits lower computational complexity compared to existing encrypted controllers implemented on LWE based schemes due to the polynomial structure of Ring-LWE. However, the structural difference introduces additional difficulties in analyzing the effect of error growth;Ring-LWE based schemes inject multiple error coefficients when encrypting a single message, which accumulate under recursive homomorphic multiplications. We show that their effect on the control performance can be arbitrarily bounded by the closed-loop stability, thus recovering the performance of the unencrypted controller. Furthermore, a novel method to "pack’’ a vector into a polynomial is presented, which enhances computational and memory efficiency when applied to the proposed encrypted controller. The effectiveness of the proposed design is demonstrated through numerical simulations. © 2024, CC BY.

关键词： Networked control systems