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ADAMANIP: ADAPTIVE ARTICULATED OBJECT MANIPULATION ENVIRONMENTS AND POLICY LEARNING

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arXiv 2025年

作者： Wang, Yuanfei Zhang, Xiaojie Wu, Ruihai Li, Yu Shen, Yan Wu, Mingdong He, Zhaofeng Wang, Yizhou Dong, Hao Center on Frontiers of Computing Studies School of Computer Science Peking University China Beijing University of Posts and Telecommunications China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China

Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated objects are endowed with diverse functional mechanisms through complex relative motions. For example, a safe consists of a door, a handle, and a lock, where the door can only be opened when the latch is unlocked. The internal structure, such as the state of a lock or joint angle constraints, cannot be directly observed from visual observation. Consequently, successful manipulation of these objects requires adaptive adjustment based on trial and error rather than a one-time visual inference. However, previous datasets and simulation environments for articulated objects have primarily focused on simple manipulation mechanisms where the complete manipulation process can be inferred from the object’s appearance. To enhance the diversity and complexity of adaptive manipulation mechanisms, we build a novel articulated object manipulation environment and equip it with 9 categories of objects. Based on the environment and objects, we further propose an adaptive demonstration collection and 3D visual diffusion-based imitation learning pipeline that learns the adaptive manipulation policy. The effectiveness of our designs and proposed method is validated through both simulation and real-world experiments. Our project page is available at: https://*** Copyright © 2025, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Causal discovery via conditional independence testing with proxy variables 24

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Causal discovery via conditional independence testing with p...

Proceedings of the 41st International Conference on Machine Learning

作者： Mingzhou Liu Xinwei Sun Yu Qiao Yizhou Wang School of Computer Science and Center on Frontiers of Computing Studies (CFCS) Peking University Sch. of Data Science Fudan University Dep. of Automation Shanghai Jiao Tong University Center on Frontiers of Computing Studies (CFCS) and School of Computer Science and Inst. for Artificial Intelligence and Nat'l Eng. Research Center of Visual Technology and Nat'l Key Lab of General Artificial Intelligence Peking University

Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data. Code is publicly available at https://***/lmz123321/proxy_causal_discovery.

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Real-time Holistic Robot Pose Estimation with Unknown States

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring

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ScoreHypo: Probabilistic Human Mesh Estimation with Hypothes...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuan Xu Xiaoxuan Ma Jiajun Su Wentao Zhu Yu Qiao Yizhou Wang Center on Frontiers of Computing Studies School of Computer Science Peking University International Digital Economy Academy (IDEA) School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Inst. for Artificial Intelligence Peking University Nat'1 Eng. Research Center of Visual Technology Nat'1 Key Lab of General Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Monocular 3D human mesh estimation is an ill-posed problem, characterized by inherent ambiguity and occlusion. While recent probabilistic methods propose generating multiple solutions, little attention is paid to obtaining high-quality estimates from them. To address this limitation, we introduce ScoreHypo, a versatile framework by first leveraging our novel HypoNet to generate multiple hy-potheses, followed by employing a meticulously designed scorer, ScoreNet, to evaluate and select high-quality esti-mates. ScoreHypo formulates the estimation process as a re-verse denoising process, where HypoNet produces a diverse set of plausible estimates that effectively align with the im-age cues. Subsequently, ScoreNet is employed to rigorously evaluate and rank these estimates based on their quality and finally identify superior ones. Experimental results demon-strate that HypoNet outperforms existing state-of-the-art probabilistic methods as a multi-hypothesis mesh estimator. Moreover, the estimates selected by ScoreNet significantly outperform random generation or simple averaging. Notably, the trained ScoreNet exhibits generalizability, as it can effectively score existing methods and significantly reduce their errors by more than 15%. Code and models are available at ht tps: / /xy02- 05. gi thub. io/ScoreHypo.

关键词： Computer vision Three-dimensional displays Accuracy Codes Noise reduction Estimation Benchmark testing

ALIGNING HUMAN MOTION GENERATION WITH HUMAN PERCEPTIONS

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arXiv 2024年

作者： Wang, Haoru Zhu, Wentao Miao, Luyi Xu, Yishu Gao, Feng Tian, Qi Wang, Yizhou Center on Frontiers of Computing Studies School of Compter Science Peking University China School of Arts Peking University China Inst. for Artificial Intelligence Peking University China Huawei Technologies Ltd China Nat’l Eng. Research Center of Visual Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China

Human motion generation is a critical task with a wide range of applications. Achieving high realism in generated motions requires naturalness, smoothness, and plausibility. Despite rapid advancements in the field, current generation methods often fall short of these goals. Furthermore, existing evaluation metrics typically rely on ground-truth-based errors, simple heuristics, or distribution distances, which do not align well with human perceptions of motion quality. In this work, we propose a data-driven approach to bridge this gap by introducing a large-scale human perceptual evaluation dataset, MotionPercept, and a human motion critic model, MotionCritic, that capture human perceptual preferences. Our critic model offers a more accurate metric for assessing motion quality and could be readily integrated into the motion generation pipeline to enhance generation quality. Extensive experiments demonstrate the effectiveness of our approach in both evaluating and improving the quality of generated human motions by aligning with human perceptions. Code and data are publicly available at https://***/. © 2024, CC BY.

关键词： Quality control

Fast Peer Adaptation with Context-aware Exploration

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arXiv 2024年

作者： Ma, Long Wang, Yuanfei Zhong, Fangwei Zhu, Song-Chun Wang, Yizhou Academy for Advanced Interdisciplinary Studies Peking University China Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU China Center on Frontiers of Computing Studies School of Computer Science Peking University China School of Intelligence Science and Technology Peking University China Inst. for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Peking University China

Fast adapting to unknown peers (partners or opponents) with different strategies is a key challenge in multi-agent games. To do so, it is crucial for the agent to probe and identify the peer's strategy efficiently, as this is the prerequisite for carrying out the best response in adaptation. However, exploring the strategies of unknown peers is difficult, especially when the games are partially observable and have a long horizon. In this paper, we propose a peer identification reward, which rewards the learning agent based on how well it can identify the behavior pattern of the peer over the historical context, such as the observation over multiple episodes. This reward motivates the agent to learn a context-aware policy for effective exploration and fast adaptation, i.e., to actively seek and collect informative feedback from peers when uncertain about their policies and to exploit the context to perform the best response when confident. We evaluate our method on diverse testbeds that involve competitive (Kuhn Poker), cooperative (PO-Overcooked), or mixed (Predator-Prey-W) games with peer agents. We demonstrate that our method induces more active exploration behavior, achieving faster adaptation and better outcomes than existing methods. © 2024, CC BY.

关键词： Contrastive Learning

Fast peer adaptation with context-aware exploration 24

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Fast peer adaptation with context-aware exploration

Proceedings of the 41st International Conference on Machine Learning

作者： Long Ma Yuanfei Wang Fangwei Zhong Song-Chun Zhu Yizhou Wang Academy for Advanced Interdisciplinary Studies Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU Center on Frontiers of Computing Studies School of Computer Science Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU School of Intelligence Science and Technology Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU Inst. for Artificial Intelligence and School of Intelligence Science and Technology Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU Center on Frontiers of Computing Studies School of Computer Science and Inst. for Artificial Intelligence and Nat'l Eng. Research Center of Visual Technology Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU

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Responsive matrix cells: A focus+context approach for exploring and editing multivariate graphs

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arXiv 2020年

作者： Horak, Tom Berger, Philip Schumann, Heidrun Dachselt, Raimund Tominski, Christian Interactive Media Lab Technische Universität Dresden Germany Inst. for Visual & Analytic Computing University of Rostock Germany

Matrix visualizations are a useful tool to provide a general overview of a graph’s structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session. Copyright © 2020, The Authors. All rights reserved.

关键词： visualization

Voice quality and its effects on university students' listening effort in a virtual seminar room

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The Journal of the Acoustical Society of America 2023年第4_SUPPLEMENT期154卷 A120-A120页

作者： Isabel S. Schiller Lukas Aspöck Carolin Breuer Jonathan Ehret Andrea Bönsch Janina Fels Torsten W Kuhlen Sabine J Schlittmeier Work and Eng. Psych. RWTH Aachen Univ. Jaegerstrasse 17-19 Aachen 52066 Germany isabel.schiller@psych.rwth-aachen.de Inst. for Hearing Technol. and Acoust. RWTH Aachen Univ. Aachen Germany Visual Computing Inst. RWTH Aachen Univ. Aachen Germany Work and Eng. Psych. RWTH Aachen Univ. Aachen Germany

A teacher’s poor voice quality may increase listening effort in pupils, but it is unclear whether this effect persists in adult listeners. Thus, the goal of this study is to examine the impact of vocal hoarseness on university students' listening effort in a virtual seminar room. An audio-visual immersive virtual reality environment is utilized to simulate a typical seminar room with common background sounds and fellow students represented as wooden mannequins. Participants wear a head-mounted display and are equipped with two controllers to engage in a dual-task paradigm. The primary task is to listen to a virtual professor reading short texts and retain relevant content information to be recalled later. The texts are presented either in a normal or an imitated hoarse voice. In parallel, participants perform a secondary task which is responding to tactile vibration patterns via the controllers. It is hypothesized that listening to the hoarse voice induces listening effort, resulting in more cognitive resources needed for primary task performance while secondary task performance is hindered. Results are presented and discussed in light of students’ cognitive performance and listening challenges in higher education learning environments.

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