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arXiv 2023年

作者： Duan, Anqing Batzianoulis, Iason Camoriano, Raffaello Rosasco, Lorenzo Pucci, Daniele Billard, Aude Robotics and Machine Intelligence Laboratory The Hong Kong Polytechnic University Hong Kong Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland Visual And Multimodal Applied Learning Laboratory Politecnico di Torino Turin Italy DIBRIS Università degli Studi di Genova Genoa Italy Laboratory for Computational and Statistical Learning IIT@MIT Istituto Italiano di Tecnologia Massachusetts Institute of Technology CambridgeMA United States Center Università di Genova Genoa Italy Dynamic Interaction Control research line Italian Institute of Technology Genoa Italy

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Learning to Sequence Multiple Tasks with Competing Constraints

Learning to Sequence Multiple Tasks with Competing Constrain...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Anqing Duan Raffaello Camoriano Diego Ferigo Yanlong Huang Daniele Calandriello Lorenzo Rosasco Daniele Pucci Dynamic Interaction Control Lab Istituto Italiano di Tecnologia Via San Quirico 19D Genoa Italy Laboratory for Computational and Statistical Learning (IIT@MIT) Istituto Italiano di Tecnologia and Massachusetts Institute of Technology Cambridge MA USA Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova Italy DIBRIS Università degli Studi di Genova Genoa Italy

Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multi-tasks sequencing with emphasis on handling the so-called competing constraints, which emerge due to the existence of the concurrent constraints from Cartesian and joint trajectories. Specifically, we explore the null space of the robot from an information-theoretic perspective in order to maintain imitation fidelity during transition between consecutive tasks. The effectiveness of the proposed method is validated through simulated and real experiments on the iCub humanoid robot.

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Hierarchical Semantic Parsing for Object Pose Estimation in Densely Cluttered Scenes

Hierarchical Semantic Parsing for Object Pose Estimation in ...

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IEEE International Conference on robotics and Automation

作者： Chi Li Jonathan Bohren Eric Carlson Gregory D. Hager Computational Interaction and Robotics Laboratory Johns Hopkins University USA

ISBN: (纸本)9781467380270

Densely cluttered scenes are composed of multiple objects which are in close contact and heavily occlude each other. Few existing 3D object recognition systems are capable of accurately predicting object poses in such scenarios. This is mainly due to the presence of objects with textureless surfaces, similar appearances and the difficulty of object instance segmentation. In this paper, we present a hierarchical semantic segmentation algorithm which partitions a densely cluttered scene into different object regions. A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class. Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains. We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes. This dataset contains five thousand scene frames and over twenty thousand labeled poses of ten common hand tools. We show that our method demonstrates improved performance of pose estimation on this new dataset compared with other state-of-the-art methods.

关键词： Parsing Object Pose

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Incremental scene understanding on dense SLAM

Incremental scene understanding on dense SLAM

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chi Li Han Xiao Keisuke Tateno Federico Tombari Nassir Navab Gregory D. Hager The Computational Interaction and Robotics Laboratory Johns Hopkins University USA Chair for Computer Aided Medical Procedures (CAMP) TU Munich Munich Germany DISI University of Bologna Italy Johns Hopkins University Baltimore MD US

ISBN: (纸本)9781509037636

We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed and tracked. Semantic labels are inferred using a multi-domain convolutional architecture which operates on the image time series and which enables efficient propagation of features as well as robust model registration. To evaluate this architecture, we introduce a large-scale RGB-D dataset JHUSEQ-25 as a new benchmark for the sequence-based scene understanding in complex and densely cluttered scenes. This dataset contains 25 RGB-D video sequences with 100,000 labeled frames in total. We validate our method on this dataset and demonstrate improved performance of semantic segmentation and 6-DoF object pose estimation compared with methods based on the single view.

关键词： Semantics Three-dimensional displays Simultaneous localization and mapping GSM Pose estimation Cameras Merging

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Development of a robotic teaching interface for human to human skill transfer

Development of a robotic teaching interface for human to hum...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Chenguang Yang Peidong Liang Arash Ajoudani Zhijun Li Antonio Bicchi Zienkiewicz Centre for Computational Engineering Swansea University UK State Key Lab of Robotics Systems Harbin Institute of Technology China Human-Robot Interfaces and Physical Interaction (HRI2) Laboratory of the Istituto Italiano di Tecnologia Genova Italy College of Automation Science and Engineering South China University of Technology Guangzhou China Interdepartmental Research Center "E. Piaggio" Faculty of Engineering University of Pisa Italy

ISBN: (纸本)9781509037636

The tutor-tutee hand-in-hand teaching may be the most effective approach for a tutee to acquire new motor skills. Repetitive nature of such procedures in a group setting usually results in a high labour cost and time inefficiency. Potential solution can be utilizing robotic platforms playing the role of tutors for demonstrating and transferring the required skills. This requires an appropriate guidance scheme to integrate the tutor's motor functionalities into the robot's control architecture. For instance, for hand-in-hand supervision of the writing task, the tutor's corrections can be applied when necessary, while a very compliant motion can be achieved if no errors are detected. Inspired by this behavior, we develop a teaching interface using a dual-arm robotic platform. In our setup, one arm is connected to the tutees arm providing guidance through a variable stiffness control approach, and the other to the tutor to capture the motion and to feedback the tutees performance in a haptic manner. The reference stiffness for the tutors arm stiffness is estimated in real-time and replicated by the tutees robotic arm. Comparative experiments have been carried out on a dual-arm Baxter robot. The results imply that the human tutor is able to intuitively transfer writing skills to the tutee and also show superior learning performance over over some conventional teaching by demonstration techniques.

关键词： Manipulators Robot sensing systems Impedance Training Muscles Manipulator sensing systems Manipulators impedance muscles Functional training Pedagogy Teaching LABOUR COSTS Human compliant motion Writing Skills Arm spiral arm Coaching Motor Skills

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Color-based hybrid reconstruction for endoscopy

Color-based hybrid reconstruction for endoscopy

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2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012

作者： Tokgozoglu, Haluk N. Meisner, Eric M. Kazhdan, Michael Hager, Gregory D. Computer Science Department Johns Hopkins University United States Computational Interaction and Robotics Laboratory Johns Hopkins University United States

ISBN: (纸本)9781467316118

Three-dimensional (3D) reconstruction of images acquired during endoscopy presents an enormous opportunity for computer vision, however reconstructing geometry from such images is challenging due to lack of features. Shape From Shading (SFS) is an approach to obtain the shape of an object from a single image, but most current methods that are applicable to endoscopy are susceptible to errors caused by surfaces with differing reflectance characteristics. Another weakness of SFS is that while it has high frequency detail information, its shape is inaccurate in the low frequency sense, which makes it difficult to compare to ground truth. Multiview reconstruction (MR), on the other hand, yields reliable shape but lacks details. In this paper, we propose a novel method to perform SFS using a color projection that minimizes intensity variance caused by differing surface characteristics. We then combine the resulting reconstruction with a multiview reconstruction obtained from bundle adjustment, and combine the two reconstructions using an approach inspired by Laplacian surface editing to get a reconstruction that is accurate both in details and overall shape. We compare our results to ground truth to show improvement over existing approaches. © 2012 IEEE.

关键词： Endoscopy

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Epipolar-based stereo tracking without explicit 3D reconstruction

Epipolar-based stereo tracking without explicit 3D reconstru...

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International Conference on Pattern Recognition

作者： Gaschler, Andre Burschka, Darius Hager, Gregory Department of Computer Science Technische Universität München München Germany Computational Interaction and Robotics Laboratory Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)9780769541099

We present a general framework for tracking image regions in two views simultaneously based on sum-of-squared differences (SSD) minimization. Our method allows for motion models up to affine transformations. Contrary to earlier approaches, we incorporate the well-known epipolar constraints directly into the SSD optimization process. Since the epipolar geometry can be computed from the image directly, no prior calibration is necessary. Our algorithm has been tested in different applications including camera localization, wide-baseline stereo, object tracking and medical imaging. We show experimental results on robustness and accuracy compared to the known ground truth given by a conventional tracking device. © 2010 IEEE.

关键词： Medical imaging

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Epipolar-Based Stereo Tracking Without Explicit 3D Reconstruction

Epipolar-Based Stereo Tracking Without Explicit 3D Reconstru...

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International Conference on Pattern Recognition

作者： Andre Gaschler Darius Burschka Gregory Hager Department of Computer Science Technische Universitat Munchen Munchen Germany Computational Interaction and Robotics Laboratory Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781424475421

关键词： Tracking Cameras Three dimensional displays Geometry Stereo vision Robustness Argon

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Towards “real-time” tool-tissue interaction detection in robotically assisted laparoscopy

Towards “real-time” tool-tissue interaction detection in r...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Sandrine Voros Gregory D. Hager Computational Interaction and Robotics Laboratory Johns Hopkins University Baltimore MD USA

The real-time detection of surgeonpsilas action could be very useful for intelligent intraoperative assistance. Robotic assisted surgery provides quantified information that can be used to detect these actions. In this paper we present our attempt at spotting tool-tissue interactions from real surgery data, in the context of robotic assisted laparoscopy with the daVincitrade system, using both kinematic information and visual information. Our early results are encouraging in this very challenging context.

关键词： Laparoscopes Surgery Surges robotics and automation Kinematics Surgical instruments Intelligent robots Data mining Feature extraction Endoscopes

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Vision-based 3D scene analysis for driver assistance

Vision-based 3D scene analysis for driver assistance

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2005 IEEE International Conference on robotics and Automation

作者： Burschka, Darius Hager, Gregory D. Computational Interaction and Robotics Laboratory Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)078038914X

We present a vision-based system for traffic sign detection and ego-motion estimation in road scenarios. The system is capable of autonomous scene reconstruction and classification. It is used to pre-select candidate surfaces in the vicinity of the road that should be inspected more closely by a sign recognition system. We compare two approaches based on a binocular and a monocular camera system, respectively. We discuss their advantages and disadvantages for applications in driver assistance systems. © 2005 IEEE.

关键词： Computer vision

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