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arXiv 2018年

作者： Englert, Peter Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany

The transfer of a robot skill between different geometric environments is non-trivial since a wide variety of environments exists, sensor observations as well as robot motions are high-dimensional, and the environment might only be partially observed. We consider the problem of extracting a low-dimensional description of the manipulated environment in form of a kinematic model. This allows us to transfer a skill by defining a policy on a prototype model and morphing the observed environment to this prototype. A deep neural network is used to map depth image observations of the environment to morphing parameter, which include transformation and configuration parameters of the prototype model. Using the concatenation property of affine transformations and the ability to convert point clouds to depth images allows to apply the network in an iterative manner. The network is trained on data generated in a simulator and on augmented data that is created by using network predictions. The algorithm is evaluated on different tasks, where it is shown that iterative predictions lead to a higher accuracy than one-step predictions. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks

Prediction of Human Full-Body Movements with Motion Optimiza...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Philipp Kratzer Marc Toussaint Jim Mainprice Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose a prediction framework that decouples short-term prediction, linked to internal body dynamics, and long-term prediction, linked to the environment and task constraints. In this work we investigate encoding short-term dynamics in a recurrent neural network, while we account for environmental constraints, such as obstacle avoidance, using gradient-based trajectory optimization. Experiments on real motion data demonstrate that our framework improves the prediction with respect to state-of-the-art motion prediction methods, as it accounts to beforehand unseen environmental structures. Moreover we demonstrate on an example, how this framework can be used to plan robot trajectories that are optimized to coordinate with a human partner.

关键词： Optimization Trajectory Predictive models Recurrent neural networks Collision avoidance Robot kinematics

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Touch based POMDP manipulation via sequential submodular optimization

Touch based POMDP manipulation via sequential submodular opt...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ngo Anh Vien Marc Toussaint Machine Learning & Robotics Lab University of Stuttgart Germany

Exploiting the submodularity of entropy-related objectives has recently led to a series of successes in machine learning and sequential decision making. Its generalized framework, adaptive submodularity, has later been introduced to deal with uncertainty and partially observability, achieving near-optimal performance with simple greedy policies. As a consequence, adaptive submodularity is in principle a promising candidate for efficient touch-based localization in robotics. However, applying that method directly on the motion level shows poor scaling with the dimensionality of the system. Being motivated by hierarchical partially observable Markov decision process (POMDP) planning, we integrate an action hierarchy into the existing adaptive submodularity framework. The proposed algorithm is expected to effectively generate uncertainty-reducing actions with the help from an action hierarchy. Experimental results on both, a simulated robot and a Willow Garage PR2 platform, demonstrate the efficiency of our algorithm.

关键词： Robot sensing systems Uncertainty Trajectory Optimization Planning Yttrium

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Relational Activity Processes for Modeling Concurrent Cooperation

Relational Activity Processes for Modeling Concurrent Cooper...

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IEEE International Conference on robotics and Automation

作者： Marc Toussaint Thibaut Munzer Yoan Mollard Li Yang Wu Ngo Anh Vien Manuel Lopes Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781467380270

In human-robot collaboration, multi-agent domains, or single-robot manipulation with multiple end-effectors, the activities of the involved parties are naturally concurrent. Such domains are also naturally relational as they involve objects, multiple agents, and models should generalize over objects and agents. We propose a novel formalization of relational concurrent activity processes that allows us to transfer methods from standard relational MDPs, such as Monte-Carlo planning and learning from demonstration, to concurrent cooperation domains. We formally compare the formulation to previous propositional models of concurrent decision making and demonstrate planning and learning from demonstration methods on a real-world human-robot assembly task.

关键词： Processes Modeling Concurrent

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Identification of unmodeled objects from symbolic descriptions

arXiv

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arXiv 2017年

作者： Baisero, Andrea Otte, Stefan Englert, Peter Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany

Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is primarily based on symbolic abstractions of object properties, rather than precise quantitative measures. A comprehensive robotic framework thus requires an integrated communication module which is able to establish a link and convert between perceptual and abstract information. The ability to interpret composite symbolic descriptions enables an autonomous agent to a) operate in unstructured and cluttered environments, in tasks which involve unmodeled or never seen before objects;and b) exploit the aggregation of multiple symbolic properties as an instance of ensemble learning, to improve identification performance even when the individual predicates encode generic information or are unprecisely grounded. We propose a discriminative probabilistic model which interprets symbolic descriptions to identify the referent object contextually w.r.t. the structure of the environment and other objects. The model is trained using a collected dataset of identifications, and its performance is evaluated by quantitative measures and a live demo developed on the PR2 robot platform, which integrates elements of perception, object extraction, object identification and grasping. Copyright © 2017, The Authors. All rights reserved.

关键词： Autonomous agents

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Int-HRL: towards intention-based hierarchical reinforcement learning

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Neural Computing and Applications 2024年 1-12页

作者： Penzkofer, Anna Schaefer, Simon Strohm, Florian Bâce, Mihai Leutenegger, Stefan Bulling, Andreas Institute for Visualisation and Interactive Systems University of Stuttgart Pfaffenwaldring 5A Stuttgart70569 Germany Machine Learning for Robotics Technical University of Munich Boltzmannstrasse 3 Munich85748 Germany Department of Computer Science KU Leuven box 2600 Andreas Vesaliusstraat 13 Leuven3000 Belgium

While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the learning process. We show that intentions of human players, i.e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma’s Revenge–one of the most challenging RL tasks in the Atari2600 game suite. We propose Int-HRL: Hierarchical RL with intention-based sub-goals that are inferred from human eye gaze. Our novel sub-goal extraction pipeline is fully automatic and replaces the need for manual sub-goal annotation by human experts. Our evaluations show that replacing hand-crafted sub-goals with automatically extracted intentions leads to an HRL agent that is significantly more sample efficient than previous methods. © The Author(s) 2024.

关键词： Deep reinforcement learning

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THE MULTIPLE VOICES OF MUSICAL EMOTIONS: SOURCE SEPARATION FOR IMPROVING MUSIC EMOTION RECOGNITION MODELS AND THEIR INTERPRETABILITY 21

THE MULTIPLE VOICES OF MUSICAL EMOTIONS: SOURCE SEPARATION F...

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21st International Society for Music Information Retrieval Conference, ISMIR 2020

作者： de Berardinis, Jacopo Cangelosi, Angelo Coutinho, Eduardo Machine Learning and Robotics Group University of Manchester United Kingdom Applied Music Research Lab University of Liverpool United Kingdom

ISBN: (纸本)9780981353708

Despite the manifold developments in music emotion recognition and related areas, estimating the emotional impact of music still poses many challenges. These are often associated to the complexity of the acoustic codes to emotion and the lack of large amounts of data with robust golden standards. In this paper, we propose a new computational model (EmoMucs) that considers the role of different musical voices in the prediction of the emotions induced by music. We combine source separation algorithms for breaking up music signals into independent song elements (vocals, bass, drums, other) and end-to-end state-of-the-art machine learning techniques for feature extraction and emotion modelling (valence and arousal regression). Through a series of computational experiments on a benchmark dataset using source-specialised models trained independently and different fusion strategies, we demonstrate that EmoMucs outperforms state-of-the-art approaches with the advantage of providing insights into the relative contribution of different musical elements to the emotions perceived by listeners. © Jacopo de Berardinis, Angelo Cangelosi, Eduardo Coutinho.

关键词： Emotion Recognition

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Feature discovery for sequential prediction of monophonic music 18

Feature discovery for sequential prediction of monophonic mu...

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18th International Society for Music Information Retrieval Conference, ISMIR 2017

作者： Langhabel, Jonas Lieck, Robert Toussaint, Marc Rohrmeier, Martin Department of Computer Science TU Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany Systematic Musicology and Music Cognition TU Dresden Germany Digital and Cognitive Musicology Lab EPFL Switzerland

ISBN: (纸本)9789811151798

learning a model for sequential prediction of symbolic music remains an open challenge. An important special case is the prediction of pitch sequences based on a corpus of monophonic music. We contribute to this line of research in two respects: (1) Our models improve the stateof- the-art performance. (2) Our method affords learning interpretable models by discovering an explicit set of relevant features. We discover features using the PULSE learning framework, which repetitively suggests new candidate features using a generative operation and selects features while optimizing the underlying model. Defining a domain-specific generative operation allows to combine multiple music-theoretically motivated features in a unified model and to control their interaction on a fine-grained level. We evaluate our models on a set of benchmark corpora of monophonic chorales and folk songs, outperforming previous work. Finally, we discuss the characteristics of the discovered features from a musicological perspective, giving concrete examples. © 2019 Jonas Langhabel, Robert Lieck, Marc Toussaint, Martin Rohrmeier.

关键词： Forecasting

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Reactive phase and task space adaptation for robust motion execution

Reactive phase and task space adaptation for robust motion e...

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Peter Englert Marc Toussaint Machine Learning and Robotics Lab Universität Stuttgart Germany

An essential aspect for making robots succeed in real-world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform.

关键词： Aerospace electronics Planning Trajectory Robot kinematics Dynamics Equations

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Active Inverse Model learning with Error and Reachable Set Estimates

Active Inverse Model Learning with Error and Reachable Set E...

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

作者： Danny Driess Syn Schmitt Marc Toussaint Machine Learning and Robotics Lab University of Stuttgart Germany Biomechanics and Biorobotics Group University of Stuttgart Germany

In this work, we propose a framework to learn an inverse model of redundant systems. We address three problems. By formalizing what it actually means to learn an inverse model, we derive a method where the inverse model, represented as a neural network, is learned by minimizing an upper bound on the real performance error, which is provided by a forward model (kernel regression or Gaussian process) learned on the currently available data. Most machine learning methods focus on learning the mapping of the function. For inverse models, it is, however, crucial to know the reachable set of the true forward model, since this becomes the domain of the inverse. Therefore, we secondly propose a method to estimate the reachable set of the system. Finally, we develop an active exploration strategy that is based on maximizing a lower bound on the true fill-distance to efficiently generate the data in the high dimensional input space. A key feature of our method is that the resulting learned inverse model provides error bounds on its *** an application point of view, this work is motivated by learning to control musculoskeletal systems. In the experiments, we show for both a simulated model of a human arm with six muscles and a real muscle-driven robot that the proposed method is able to learn the reachable set of these systems as well as a policy that enables to accurately control the position.

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