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Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning

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Enabling Robots to Understand Incomplete Natural Language In...

IEEE International Conference on robotics and Automation (ICRA)

作者： Haonan Chen Hao Tan Alan Kuntz Mohit Bansal Ron Alterovitz Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill NC USA Robotics Center and School of Computing University of Utah Salt Lake City UT USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Enabling robots to understand instructions provided via spoken natural language would facilitate interaction between robots and people in a variety of settings in homes and workplaces. However, natural language instructions are often missing information that would be obvious to a human based on environmental context and common sense, and hence does not need to be explicitly stated. In this paper, we introduce Language-Model-based Commonsense Reasoning (LMCR), a new method which enables a robot to listen to a natural language instruction from a human, observe the environment around it, and automatically fill in information missing from the instruction using environmental context and a new commonsense reasoning approach. Our approach first converts an instruction provided as unconstrained natural language into a form that a robot can understand by parsing it into verb frames. Our approach then fills in missing information in the instruction by observing objects in its vicinity and leveraging commonsense reasoning. To learn commonsense reasoning automatically, our approach distills knowledge from large unstructured textual corpora by training a language model. Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.

关键词： Natural languages Robot sensing systems Cognition Task analysis Semantics Neural networks

Active Learning of Probabilistic Movement Primitives

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Active Learning of Probabilistic Movement Primitives

IEEE-RAS International Conference on Humanoid Robots

作者： Adam Conkey Tucker Hermans School of Computing Robotics Center: University of Utah USA NVIDIA USA

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

A Probabilistic Movement Primitive (ProMP) defines a distribution over trajectories with an associated feedback policy. ProMPs are typically initialized from human demonstrations and achieve task generalization through probabilistic operations. However, there is currently no principled guidance in the literature to determine how many demonstrations a teacher should provide and what constitutes a “good” demonstration for promoting generalization. In this paper, we present an active learning approach to learning a library of ProMPs capable of task generalization over a given space. We utilize uncertainty sampling techniques to generate a task instance for which a teacher should provide a demonstration. The provided demonstration is incorporated into an existing ProMP if possible, or a new ProMP is created from the demonstration if it is determined that it is too dissimilar from existing demonstrations. We provide a qualitative comparison between common active learning metrics; motivated by this comparison we present a novel uncertainty sampling approach named “Greatest Mahalanobis Distance.” We perform grasping experiments on a real KUKA robot and show our novel active learning measure achieves better task generalization with fewer demonstrations than a random sampling over the space.

关键词： Task analysis Robots Probabilistic logic Trajectory Uncertainty Grasping Libraries

Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning

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arXiv 2023年

作者： Sivarajkumar, Sonish Tandale, Pratyush Bhardwaj, Ankit Johnson, Kipp W. Titus, Anoop Glicksberg, Benjamin S. Khader, Shameer Yadav, Kamlesh K. Subramanian, Lakshminarayanan Molecular Robotics Kerala India School of Computing and Information University of Pittsburgh Pennsylvania PA United States Health Informatics & Data Science Georgetown University WashingtonDC United States Department of Computer Science Courant Institute of Mathematical Sciences New York University New YorkNY United States Institute for Next Generation Healthcare Mount Sinai Health System New YorkNY United States Department of Preventive Cardiology DeBakey Heart & Vascular Center Houston Methodist HoustonTX United States Hasso Plattner Institute for Digital Health Icahn School of Medicine at Mount Sinai New YorkNY United States Faculty of Medicine Imperial College London London United Kingdom School of Engineering Medicine Texas A&M University HoustonTX United States Department of Translational Medical Sciences Center for Genomic and Precision Medicine Texas A&M University HoustonTX United States

Transcription factors (TFs) play a vital role in the regulation of gene expression thereby making them critical to many cellular processes. In this study, we used graph machine learning methods to create a compendium of TF cascades using data extracted from the STRING database. A TF cascade is a sequence of TFs that regulate each other, forming a directed path in the TF network. We constructed a knowledge graph of 81,488 unique TF cascades, with the longest cascade consisting of 62 TFs. Our results highlight the complex and intricate nature of TF interactions, where multiple TFs work together to regulate gene expression. We also identified 10 TFs with the highest regulatory influence based on centrality measurements, providing valuable information for researchers interested in studying specific TFs. Furthermore, our pathway enrichment analysis revealed significant enrichment of various pathways and functional categories, including those involved in cancer and other diseases, as well as those involved in development, differentiation, and cell signaling. The enriched pathways identified in this study may have potential as targets for therapeutic intervention in diseases associated with dysregulation of transcription factors. We have released the dataset, knowledge graph, and graphML methods for the TF cascades, and created a website to display the results, which can be accessed by researchers interested in using this dataset. Our study provides a valuable resource for understanding the complex network of interactions between TFs and their regulatory roles in cellular processes. Copyright © 2023, The Authors. All rights reserved.

关键词： Transcription factors

Lyapunov-based reinforcement learning for decentralized multi-agent control

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arXiv 2020年

作者： Zhang, Qingrui Dong, Hao Pan, Wei School of Aeronautics and Astronautics Sun Yat-Sen University Guangzhou China Center on Frontiers of Computing Studies Peking University Beijing China Department of Cognitive Robotics Delft University of Technology Delft Netherlands

Decentralized multi-agent control has broad applications, ranging from multi-robot cooperation to distributed sensor networks. In decentralized multi-agent control, systems are complex with unknown or highly uncertain dynamics, where traditional model-based control methods can hardly be applied. Compared with model-based control in control theory, deep reinforcement learning (DRL) is promising to learn the controller/policy from data without the knowing system dynamics. However, to directly apply DRL to decentralized multi-agent control is challenging, as interactions among agents make the learning environment non-stationary. More importantly, the existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system from a control-theoretic perspective, so the learned control polices are highly possible to generate abnormal or dangerous behaviors in real applications. Hence, without stability guarantee, the application of the existing MARL algorithms to real multi-agent systems is of great concern, e.g., UAVs, robots, and power systems, etc. In this paper, we aim to propose a new MARL algorithm for decentralized multiagent control with a stability guarantee. The new MARL algorithm, termed as a multi-agent soft-actor critic (MASAC), is proposed under the well-known framework of "centralized-training-with-decentralized-execution". The closed-loop stability is guaranteed by the introduction of a stability constraint during the policy improvement in our MASAC algorithm. The stability constraint is designed based on Lyapunov’s method in control theory. To demonstrate the effectiveness, we present a multi-agent navigation example to show the efficiency of the proposed MASAC algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

Deep Learning-Based Automatic Multi-Level Airway Collapse Monitoring on Obstructive Sleep Apnea Patients

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arXiv 2024年

作者： Hsu, Ying-Chieh Liu, Stanley Yung-Chuan Huang, Chao-Jung Wu, Chi-Wei Cheng, Ren-Kai Hsu, Jane Yung-Jen Huang, Shang-Ran Cheng, Yuan-Ren Hsu, Fu-Shun Department of Biomedical Engineering National Taiwan University Taipei Taiwan Department of Otolaryngology Head and Neck Surgery Taipei Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Taipei Taiwan Tzu Chi University School of Medicine Hualien Taiwan Department of Oral & Maxillofacial Surgery Nova Southeastern University College of Dental Medicine College of Allopathic Medicine Florida United States All Vista Healthcare Center Center for AI and Advanced Robotics National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Artificial Intelligence Chang Gung University College of Intelligent Computing Taoyuan Taiwan Heroic Faith Medical Science Co. Ltd. New Taipei Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Internal Medicine Chung Shan Medical University Taichung Taiwan Hospital Development Affairs Office Chung Shan Medical University Hospital Taichung Taiwan

This study investigated the use of deep learning to identify multi-level upper airway collapses in obstructive sleep apnea (OSA) patients based on snoring sounds. We fi-ne-tuned ResNet-50 and Audio Spectrogram Transformer (AST) models using snoring recordings from 37 subjects undergoing drug-induced sleep endoscopy (DISE) between 2020 and 2021. Snoring sounds were labeled according to the VOTE (Velum, Orophar-ynx, Tongue Base, Epiglottis) classification, resulting in 259 V, 403 O, 77 T, 13 E, 1016 VO, 46 VT, 140 OT, 39 OE, 30 VOT, and 3150 non-snoring (N) 0.5-second clips. The models were trained for two multi-label classification tasks: identifying obstructions at V, O, T, and E levels, and identifying retropalatal (RP) and retroglossal (RG) obstruc-tions. Results showed AST slightly outperformed ResNet-50, demonstrating good abil-ity to identify V (F1-score: 0.71, MCC: 0.61, AUC: 0.89), O (F1-score: 0.80, MCC: 0.72, AUC: 0.94), and RP obstructions (F1-score: 0.86, MCC: 0.77, AUC: 0.97). However, both models struggled with T, E, and RG classifications due to limited data. Retrospective analysis of a full-night recording showed the potential to profile airway obstruction dynamics. We expect this information, combined with polysomnography and other clinical parameters, can aid clinical triage and treatment planning for OSA patients. © 2024, CC BY-SA.

关键词： Endoscopy

Active Learning of Probabilistic Movement Primitives

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arXiv 2019年

作者： Conkey, Adam Hermans, Tucker School of Computing Robotics Center University of Utah United States NVIDIA United States

A Probabilistic Movement Primitive (ProMP) defines a distribution over trajectories with an associated feedback policy. ProMPs are typically initialized from human demonstrations and achieve task generalization through probabilistic operations. However, there is currently no principled guidance in the literature to determine how many demonstrations a teacher should provide and what constitutes a "good" demonstration for promoting generalization. In this paper, we present an active learning approach to learning a library of ProMPs capable of task generalization over a given space. We utilize uncertainty sampling techniques to generate a task instance for which a teacher should provide a demonstration. The provided demonstration is incorporated into an existing ProMP if possible, or a new ProMP is created from the demonstration if it is determined that it is too dissimilar from existing demonstrations. We provide a qualitative comparison between common active learning metrics;motivated by this comparison we present a novel uncertainty sampling approach named "Greatest Mahalanobis Distance." We perform grasping experiments on a real KUKA robot and show our novel active learning measure achieves better task generalization with fewer demonstrations than a random sampling over the space. Copyright © 2019, The Authors. All rights reserved.

关键词： Demonstrations

Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model

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arXiv 2021年

作者： Bentley, Michael Rucker, Caleb Reddy, Chakravarthy Salzman, Oren Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States The Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN37996 United States Huntsman Cancer Institute and School of Medicine University of Utah Salt Lake CityUT84112 United States Department of Computer Science Technion - Israel Institute of Technology Technion City Haifa3200003 Israel

Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the potential to reduce the invasiveness of many medical procedures. However, inserting these needles with curved trajectories increases the risk of tissue damage due to perpendicular forces exerted on the surrounding tissue by the needle's shaft, potentially resulting in lateral shearing through tissue. Such forces can cause significant damage to surrounding tissue, negatively affecting patient outcomes. In this work, we derive a tissue and needle force model based on a Cosserat string formulation, which describes the normal forces and frictional forces along the shaft as a function of the planned needle path, friction model and parameters, and tip piercing force. We propose this new force model and associated cost function as a safer and more clinically relevant metric than those currently used in motion planning for steerable needles. We fit and validate our model through physical needle robot experiments in a gel phantom. We use this force model to define a bottleneck cost function for motion planning and evaluate it against the commonly used path-length cost function in hundreds of randomly generated 3-D environments. Plans generated with our force-based cost show a 62% reduction in the peak modeled tissue force with only a 0.07% increase in length on average compared to using the path-length cost in planning. Additionally, we demonstrate the ability to plan motions with our force-based cost function in a lung tumor biopsy scenario from a segmented computed tomography (CT) scan. By planning motions for the needle that aim to minimize the modeled needle-to-tissue force explicitly, our method plans needle paths that may reduce the risk of significant tissue damage while still reaching desired targets in the body. © 2021, CC BY.

关键词： Needles

Benchmarking in-hand manipulation

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arXiv 2020年

作者： Cruciani, Silvia Sundaralingam, Balakumar Hang, Kaiyu Kumar, Vikash Hermans, Tucker Kragic, Danica Division of Robotics Perception & Learning EECS KTH Royal Institute of Technology Stockholm Sweden Robotics Center & School of Computing University of Utah UT United States Dept. of Mechanical Engineering & Material Science Yale University New HavenCT United States Google AI NVIDIA Research

planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system’s ability to change the pose of a hand-held object by either using the fingers, environment or a combination of both. Given an object surface mesh from the YCB data-set, we provide examples of initial and goal states (i.e. static object poses and fingertip locations) for various in-hand manipulation tasks. We further propose metrics that measure the error in reaching the goal state from a specific initial state, which, when aggregated across all tasks, also serves as a measure of the system’s in-hand manipulation capability. We provide supporting software, task examples, and evaluation results associated with the benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

Deep momentum uncertainty hashing

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arXiv 2020年

作者： Fu, Chaoyou Wang, Guoli Wu, Xiang Zhang, Qian He, Ran National Laboratory of Pattern Recognition CASIA Center for Excellence in Brain Science and Intelligence Technology CAS Center for Research on Intelligent Perception and Computing CASIA School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation Tsinghua University China Horizon Robotics

Combinatorial optimization (CO) has been a hot research topic because of its theoretic and practical importance. As a classic CO problem, deep hashing aims to find an optimal code for each data from finite discrete possibilities, while the discrete nature brings a big challenge to the optimization process. Previous methods usually mitigate this challenge by binary approximation, substituting binary codes for real-values via activation functions or regularizations. However, such approximation leads to uncertainty between real-values and binary ones, degrading retrieval performance. In this paper, we propose a novel Deep Momentum Uncertainty Hashing (DMUH). It explicitly estimates the uncertainty during training and leverages the uncertainty information to guide the approximation process. Specifically, we model bit-level uncertainty via measuring the discrepancy between the output of a hashing network and that of a momentum-updated network. The discrepancy of each bit indicates the uncertainty of the hashing network to the approximate output of that bit. Meanwhile, the mean discrepancy of all bits in a hashing code can be regarded as image-level uncertainty. It embodies the uncertainty of the hashing network to the corresponding input image. The hashing bit and image with higher uncertainty are paid more attention during optimization. To the best of our knowledge, this is the first work to study the uncertainty in hashing bits. Extensive experiments are conducted on four datasets to verify the superiority of our method, including CIFAR-10, NUS-WIDE, MS-COCO, and a million-scale dataset Clothing1M. Our method achieves the best performance on all of the datasets and surpasses existing state-of-the-art methods by a large margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Combinatorial optimization