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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Y. Mollard T. Munzer A. Baisero M. Toussaint M. Lopes Flowers Team Inria France Machine Learning and Robotics Lab University of Stuttgart Germany

This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approaches such as learning from demonstration, learning from feedback and knowledge transfer. Starting from low-level demonstrations of assembly tasks, the system is able to extract a high-level relational plan of the task. A graphical user interface (GUI) allows then the user to iteratively correct the acquired knowledge by refining high-level plans, and low-level geometrical knowledge of the task. This combination leads to a faster programming phase, more precise than just demonstrations, and more intuitive than just through a GUI. A final process allows to reuse high-level task knowledge for similar tasks in a transfer learning fashion. Finally we present a user study illustrating the advantages of this approach.

关键词： Assembly Graphical user interfaces Service robots Computational modeling Context Training

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement learning

作者： Hans, Alexander Duell, Siegmund Udluft, Steffen Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Corporate Technology Munich Munich Germany

ISBN: (纸本)9781424498888

With the development of data-efficient reinforcement learning (RL) methods, a promising data-driven solution for optimal control of complex technical systems has become available. For the application of RL to a technical system, it is usually required to evaluate a policy before actually applying it to ensure it operates the system safely and within required performance bounds. In benchmark applications one can use the system dynamics directly to measure the policy quality. In real applications, however, this might be too expensive or even impossible. Being unable to evaluate the policy without using the actual system hinders the application of RL to autonomous controllers. As a first step toward agent self-assessment, we deal with discrete MDPs in this paper. We propose to use the value function along with its uncertainty to assess a policy's quality and show that, when dealing with an MDP estimated from observations, the value function itself can be misleading. We address this problem by determining the value function's uncertainty through uncertainty propagation and evaluate the approach using a number of benchmark applications. © 2011 IEEE.

关键词： Reinforcement learning

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Entropy-based strategies for physical exploration of the environment's degrees of freedom

Entropy-based strategies for physical exploration of the env...

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Stefan Otte Johannes Kulick Marc Toussaint Oliver Brock Machine Learning and Robotics Lab Universität Stuttgart Germany Robotics and Biology Laboratory Technische Universität Berlin Germany

Physical exploration refers to the challenge of autonomously discovering and learning how to manipulate the environment's degrees of freedom (DOF)-by identifying promising points of interaction and pushing or pulling object parts to reveal DOF and their properties. Recent existing work focused on sub-problems like estimating DOF parameters from given data. Here, we address the integrated problem, focusing on the higher-level strategy to iteratively decide on the next exploration point before applying motion generation methods to execute the explorative action and data analysis methods to interpret the feedback. We propose to decide on exploration points based on the expected information gain, or change in entropy in the robot's current belief (uncertain knowledge) about the DOF. To this end, we first define how we represent such a belief. This requires dealing with the fact that the robot initially does not know which random variables (which DOF, and depending on their type, which DOF properties) actually exist. We then propose methods to estimate the expected information gain for an exploratory action. We analyze these strategies in simple environments and evaluate them in combination with full motion planning and data analysis in a physical simulation environment.

关键词： Joints Entropy Robots Random variables Trajectory Uncertainty Friction

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Safe Multi-Agent Reinforcement learning for Behavior-Based Cooperative Navigation

arXiv

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arXiv 2023年

作者： Dawood, Murad Pan, Sicong Dengler, Nils Zhou, Siqi Schoellig, Angela P. Bennewitz, Maren The Humanoid Robots Lab University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence and the Center for Robotics Bonn Germany The Learning Systems and Robotics lab The Technical University of Munich Germany

In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning (MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training. © 2023, CC0.

关键词： Adversarial machine learning

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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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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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Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty

arXiv

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arXiv 2020年

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

— Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulting smooth trajectory optimization. The expressive power of logic allows LGP for handling complex, large-scale sequential manipulation and tool-use planning problems. In this paper, we extend the LGP formulation to stochastic domains. Based on the control-inference duality, we interpret LGP in a stochastic domain as fitting a mixture of Gaussians to the posterior path distribution, where each logic profile defines a single Gaussian path distribution. The proposed framework enables a robot to prioritize various interaction modes and to acquire interesting behaviors such as contact exploitation for uncertainty reduction, eventually providing a composite control scheme that is reactive to disturbance. The supplementray video can be found at https://***/CEaJdVlSZyo. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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A Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty

A Probabilistic Framework for Constrained Manipulations and ...

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IEEE International Conference on robotics and Automation (ICRA)

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

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulting smooth trajectory optimization. The expressive power of logic allows LGP for handling complex, large-scale sequential manipulation and tool-use planning problems. In this paper, we extend the LGP formulation to stochastic domains. Based on the control-inference duality, we interpret LGP in a stochastic domain as fitting a mixture of Gaussians to the posterior path distribution, where each logic pro le defines a single Gaussian path distribution. The proposed framework enables a robot to prioritize various interaction modes and to acquire interesting behaviors such as contact exploitation for uncertainty reduction, eventually providing a composite control scheme that is reactive to disturbance.

关键词： Robots Planning Trajectory optimization Skeleton Programming

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Guided Decoding for Robot On-line Motion Generation and Adaption 23

Guided Decoding for Robot On-line Motion Generation and Adap...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Chen, Nutan Cseke, Botond Aljalbout, Elie Paraschos, Alexandros Alles, Marvin Van Der Smagt, Patrick Machine Learning Research Lab Volkswagen Group Germany Robotics and Perception Group Department of Informatics Switzerland Uzh Eth Zurich Department of Neuroinformatics Switzerland Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350373578

We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories. We train a transformer architecture, based on conditional variational autoencoder, on a large dataset of simulated trajectories used as demonstrations. Our architecture learns essential motion generation skills from these demonstrations and is able to adapt them to meet auxiliary tasks. Additionally, our approach implements auto-regressive motion generation to enable real-time adaptations, as, for example, introducing or changing via-points, and velocity and acceleration constraints. Using beam search, we present a method for further adaption of our motion generator to avoid obstacles. We show that our model successfully generates motion from different initial and target points and that is capable of generating trajectories that navigate complex tasks across different robotic platforms. © 2024 IEEE.

关键词： Trajectories

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SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo

SimpleMapping: Real-Time Visual-Inertial Dense Mapping with ...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Yingye Xin Xingxing Zuo Dongyue Lu Stefan Leutenegger Smart Robotics Lab Technical University of Munich Germany Munich Center for Machine Learning (MCML) Germany

We present a real-time visual-inertial dense mapping method capable of performing incremental 3D mesh reconstruction with high quality using only sequential monocular images and inertial measurement unit (IMU) readings. 6-DoF camera poses are estimated by a robust feature-based visual-inertial odometry (VIO), which also generates noisy sparse 3D map points as a by-product. We propose a sparse point aided multi-view stereo neural network (SPA-MVSNet) that can effectively leverage the informative but noisy sparse points from the VIO system. The sparse depth from VIO is firstly completed by a single-view depth completion network. This dense depth map, although naturally limited in accuracy, is then used as a prior to guide our MVS network in the cost volume generation and regularization for accurate dense depth prediction. Predicted depth maps of keyframe images by the MVS network are incrementally fused into a global map using TSDF-Fusion. We extensively evaluate both the proposed SPA-MVSNet and the entire dense mapping system on several public datasets as well as our own dataset, demonstrating the system’s impressive generalization capabilities and its ability to deliver high-quality 3D reconstruction online. Our proposed dense mapping system achieves a 39.7% improvement in F-score over existing systems when evaluated on the challenging scenarios of the EuRoC dataset.

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