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2020 Conference on Artificial Life, ALIFE 2020

作者： Talamini, Jacopo Medvet, Eric Bartoli, Alberto De Lorenzo, Andrea Machine Learning Lab. Department of Engineering and Architecture University of Trieste Italy Evolutionary Robotics and Artificial Life Lab. Department of Engineering and Architecture University of Trieste Italy

We consider a multi-agent system in which the individual goal is to collect resources, but where the amount of collected resources depends also on others decision. Agents can communicate and can take advantage of being communicated other agents’ plan: therefore they may develop more profitable strategies. We wonder if some kind of collective behaviour, with respect to communication, emerges in this system without being explicitly promoted. To investigate this aspect, we design three different scenarios, respectively a cooperative, a competitive, and a mixed one, in which agents’ behaviors are individually learned by means of reinforcement learning. We consider different strategies concerning communication and learning, including no-communication, always-communication, and optional-communication. Experimental results show that always-communication leads to a collective behaviour with the best results in terms of both overall earned resources and equality between agents. On the other hand optional-communication strategy leads to similar collective strategies in some of these scenarios, but in other scenarios some agents develop individual behaviours that oppose to the collective welfare and thus result in high inequality. © 2020 Massachusetts Institute of Technology.

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Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features

Classification of benign and malignant breast tumors in ultr...

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3rd International Conference on Intelligent Human-machine Systems and Cybernetics, IHMSC 2011

作者： Liao, Renjie Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing 100191 China Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9780769544441

We establish a new set of features for differentiating benign from malignant breast lesions using ultrasound (US) images. Two types of features (sonographic and textural features) are considered. Among them, three sonographic features are novel. Sonograms of 321 pathologically proven breast cases are analyzed and classified into benign and malignant categories. The discrimination capability of the extracted features are evaluated using the support vector machines (SVM) in comparison with the results obtained from artificial neural networks (ANN) and K-nearest neighbor (KNN) classifier. The simulations demonstrate that the proposed algorithm can be an integral part to US computer-aided diagnosis (CAD) systems for breast cancer or an independent program to help accurately distinguish benign solid breast nodules from malignant nodules. © 2011 IEEE.

关键词： Computer aided diagnosis

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Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Tuscher Julian Hörz Danny Driess Marc Toussaint sereact Machine Learning and Robotics Lab University of Stuttgart Max-Planck Institute for Intelligent Systems Stuttgart Learning and Intelligent Systems TU Berlin

Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.

关键词： Training Target tracking Automation Trajectory planning Perturbation methods Conferences Grasping

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An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

An Optimal Algorithm to Solve the Combined Task Allocation a...

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

作者： Christian Henkel Jannik Abbenseth Marc Toussaint Machine Learning and Robotics Lab University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany

We consider multi-agent transport task problems where, e.g. in a factory setting, items have to be delivered from a given start to a goal pose while the delivering robots need to avoid collisions with each other on the *** introduce a Task Conflict-Based Search (TCBS) Algorithm to solve the combined delivery task allocation and multiagent path planning problem optimally. The problem is known to be NP-hard and the optimal solver cannot scale. However, we introduce it as a baseline to evaluate the sub-optimality of other approaches. We show experimental results that compare our solver with different sub-optimal ones in terms of regret.

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An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

arXiv

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arXiv 2019年

作者： Henkel, Christian Abbenseth, Jannik Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany

— We consider multi-agent transport task problems where, e.g. in a factory setting, items have to be delivered from a given start to a goal pose while the delivering robots need to avoid collisions with each other on the floor. We introduce a Task Conflict-Based Search (TCBS) Algorithm to solve the combined delivery task allocation and multi-agent path planning problem optimally. The problem is known to be NP-hard and the optimal solver cannot scale. However, we introduce it as a baseline to evaluate the sub-optimality of other approaches. We show experimental results that compare our solver with different sub-optimal ones in terms of regret. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning

arXiv

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arXiv 2020年

作者： Toussaint, Marc Ha, Jung-Su Driess, Danny Machine Learning & Robotics Lab University Stuttgart Stuttgart70569 Germany Max Planck Institute for Intelligent Systems Stuttgart70569 Germany

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose path descriptions of physics which directly allow us to leverage optimization methods to solve planning problems, using multi-physics descriptions that enable the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target. Copyright © 2020, The Authors. All rights reserved.

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Environment modeling for maintenance automation-a next-best-view approach for combining space exploration and object recognition tasks

Environment modeling for maintenance automation-a next-best-...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Christian Friedrich Viktor Zielke Marc Toussaint Armin Lechler Alexander Verl Institute of Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany Institute of Machine Learning and Robotics University Stuttgart Stuttgart Germany

In order to enable robot systems to solve complex tasks autonomously, it is essential to model the environment in a sufficient way. This work describes a new approach to create a consistent environment model, using next-best-view planning which combines the optimization of the space exploration and the object recognition task. The approach is designed for the automation of maintenance tasks, which is a challenging task for autonomous robotic systems. After a general overview of the developed system, we explain the theoretic foundations for building a consistent environment model which can be used for manipulation planning. The concept is validated by means of application-oriented examples.

关键词： Solid modeling Maintenance engineering Automation Robot sensing systems Planning

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Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models

arXiv

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arXiv 2022年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim The Machine Learning and Robotics Lab University of Stuttgart Germany The Humans to Robots Motions Research Group University of Stuttgart Germany The Learning and Intelligent Systems Lab TU Berlin Germany

Numerical optimization has become a popular approach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efficiency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modifiers to a differentiable human predictive model based on a dedicated recurrent neural network. These modifiers allow to change the human prediction within motion optimization. We empirically evaluate our method using the publicly available MoGaze [1] dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other. © 2022, CC BY.

关键词： Forecasting

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Camera-Based Belief Space Planning in Discrete Partially-Observable Domains

Camera-Based Belief Space Planning in Discrete Partially-Obs...

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

作者： Janis Eric Freund Camille Phiquepal Andreas Orthey Marc Toussaint Technical University of Berlin Germany Machine Learning & Robotics Lab University of Stuttgart Germany Realtime Robotics Inc Boston MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Robots often have to operate in discrete partially observable worlds, where the state of the world is only observable at runtime. To react to different world states, robots need contingencies. To find contingencies, prior work developed the path tree optimization (PTO) method, which computes motion contingencies by constructing a tree of motion paths in belief space. In this paper, we extend upon PTO by enabling camera-based belief space planning through an extension of the open motion planning library (OMPL). By leveraging this extension, we develop an improved camera-based state sampler and an efficient open-source implementation of PTO. This version of PTO supports a virtual camera, non-euclidean state spaces, and different state samplers. We evaluate this improved version of PTO on four realistic scenarios with a virtual camera in up to 10-dimensional state spaces. In our evaluations, we compare PTO both with a default and with the new camera-based state sampler. The results indicate that the camera-based state sampler improves success rates in 3 out of 4 scenarios while having a significant lower memory footprint. Our work thus makes an important step in advancing belief-space planning and provides researchers with an open source tool to use, modify, and benchmark belief-space planning methods.

关键词： Technological innovation Runtime Contingency management Robot vision systems Benchmark testing Cameras Libraries Planning Optimization Intelligent robots

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What is the basic semantic unit of chinese language? A computational approach based on topic models

What is the basic semantic unit of chinese language? A compu...

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12th Meeting on Mathematics of Language, MOL 12

作者： Zhao, Qi Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University Beijing China Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9783642232107

Chinese language has been generally regarded as a Subject-Verb -Object (SVO) language and the basic semantic unit is the Chinese word that is usually consisted by two or more Chinese characters. However, word-centered structure of Chinese language has been controversial in linguistics. Some recent research in computational linguistics in Chinese language suggests that the character-based models perform better than the word-based models in some applications such word segmentation. In this paper, the word-based topic models and the character-based models are tested for modeling Chinese language, respectively. By empirical studies, we demonstrated the effectiveness of using Chinese characters as the basic semantic units. These two models have close performance in text classifications while the character-based model has a better quality in language modeling and a much smaller vocabulary. By testing on a bilingual corpus, three independent topic models based on Chinese words, Chinese characters and English words are trained and compared to each other. we verify the capability of topic models in modeling semantics by experiments across Chinese and English. The classification accuracy can also be boosted up by aggregating the classification results from the three independent topic models. © 2011 Springer-Verlag.

关键词： Computational linguistics

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