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arXiv 2022年

作者： Phiquepal, Camille Orthey, Andreas Viennot, Nicolas Toussaint, Marc Machine Learning & Robotic Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Germany

Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a path-tree in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm assumes a deterministic observation model and is composed of three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Physically-Consistent Parameter Identification of Robots in Contact

arXiv

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arXiv 2024年

作者： Khorshidi, Shahram Dawood, Murad Nederkorn, Benno Bennewitz, Maren Khadiv, Majid Humanoid Robots Lab University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence the Center for Robotics Bonn Germany Roboverse Reply Munich Germany Germany

Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contacts with the environment. Classically, robots’ inertial parameters are obtained from CAD models that are not precise (and sometimes not available, e.g., Spot from Boston Dynamics), hence requiring identification. To do that, existing methods require access to contact force measurement, a modality not present in modern quadruped and humanoid robots. This paper presents an alternative technique that utilizes joint current/torque measurements —a standard sensing modality in modern robots— to identify inertial parameters without requiring direct contact force measurements. By projecting the whole-body dynamics into the null space of contact constraints, we eliminate the dependency on contact forces and reformulate the identification problem as a linear matrix inequality that can handle physical and geometrical constraints. We compare our proposed method against a common black-box identification method using a deep neural network and show that incorporating physical consistency significantly improves the sample efficiency and generalizability of the model. Finally, we validate our method on the Spot quadruped robot across various locomotion tasks, showcasing its accuracy and generalizability in real-world scenarios over different gaits. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects

Learning a Shape-Conditioned Agent for Purely Tactile In-Han...

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

作者： Johannes Pitz Lennart Röstel Leon Sievers Darius Burschka Berthold Bäuml Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany DLR Institute of Robotics & Mechatronics (German Aerospace Center) Machine Vision and Perception Group Technical University of Munich

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent supervision of the object state from visual sensors. This is far from human capabilities and from what is needed in real-world applications. In this work, we address this gap by training shape-conditioned agents to reorient diverse objects in hand, relying purely on tactile feedback (via torque and position measurements of the fingers’ joints). To achieve this, we propose a learning framework that exploits shape information in a reinforcement learning policy and a learned state estimator. We find that representing 3D shapes by vectors from a fixed set of basis points to the shape’s surface, transformed by its predicted 3D pose, is especially helpful for learning dexterous in-hand manipulation. In simulation and real-world experiments, we show the reorientation of many objects with high success rates, on par with state-of-the-art results obtained with specialized single-object agents. Moreover, we show generalization to novel objects, achieving success rates of ~90% even for non-convex ***: https://***/manipulation/iros24

关键词： Training Visualization Three-dimensional displays Torque Shape Tactile sensors Reinforcement learning Position measurement Vectors Sensors

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Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits

arXiv

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arXiv 2024年

作者： Yao, Shaoxiong Pan, Sicong Bennewitz, Maren Hauser, Kris University of Illinois at Urbana-Champaign IL United States Humanoid Robots Lab University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn Germany

Fruit monitoring plays an important role in crop management, and rising global fruit consumption combined with labor shortages necessitates automated monitoring with robots. However, occlusions from plant foliage often hinder accurate shape and pose estimation. Therefore, we propose an active fruit shape and pose estimation method that physically manipulates occluding leaves to reveal hidden fruits. This paper introduces a framework that plans robot actions to maximize visibility and minimize leaf damage. We developed a novel scene-consistent shape completion technique to improve fruit estimation under heavy occlusion and utilize a perception-driven deformation graph model to predict leaf deformation during planning. Experiments on artificial and real sweet pepper plants demonstrate that our method enables robots to safely move leaves aside, exposing fruits for accurate shape and pose estimation, outperforming baseline methods. Project page: https://***/lmap-ssc/. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

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Target-Aware Implicit Mapping for Agricultural Crop Inspection

Target-Aware Implicit Mapping for Agricultural Crop Inspecti...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shane Kelly Alessandro Riccardi Elias Marks Federico Magistri Tiziano Guadagnino Margarita Chli Cyrill Stachniss Vision for Robotics Lab ETH Zurich University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany Department of Engineering Science University of Oxford UK

Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural environments to create dense maps of crop rows with high enough fidelity to be useful for automated crop inspection. Specifically, we map strawberry and sweet pepper crop rows using RGB images captured by a wheeled mobile field robot inside a greenhouse and then use this data to build 3D maps to document the development of plants and fruits. Our Target-Aware Implicit Mapping system (TAIM) uses a SLAM-based pose initialization strategy for robust pose convergence, an efficient information-guided training sample selection framework for faster loss reduction, and focuses on exploiting training samples for fruit regions of the scene, which are critical for crop inspection tasks, to create more accurate maps in less time.

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learning to execute: efficiently learning universal plan-conditioned policies in robotics 21

Learning to execute: efficiently learning universal plan-con...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ingmar Schubert Danny Driess Ozgur S. Oguz Marc Toussaint Learning and Intelligent Systems Group TU Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781713845393

Applications of Reinforcement learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is imprecise or wrong. In this sense, the respective strengths and weaknesses of RL and model-based planners are complementary. In the present work, we investigate how both approaches can be integrated into one framework that combines their strengths. We introduce learning to Execute (L2E), which leverages information contained in approximate plans to learn universal policies that are conditioned on plans. In our robotic manipulation experiments, L2E exhibits increased performance when compared to pure RL, pure planning, or baseline methods combining learning and planning.

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learning to execute: Efficiently learning universal plan-conditioned policies in robotics

arXiv

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arXiv 2021年

作者： Schubert, Ingmar Driess, Danny Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group Tu Berlin Germany Machine Learning and Robotics Lab University of Stuttgart Germany

Applications of Reinforcement learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenarios, making model-based approaches like planning a data-efficient alternative. Still, the performance of these methods suffers if the model is imprecise or wrong. In this sense, the respective strengths and weaknesses of RL and modelbased planners are complementary. In the present work, we investigate how both approaches can be integrated into one framework that combines their strengths. We introduce learning to Execute (L2E), which leverages information contained in approximate plans to learn universal policies that are conditioned on plans. In our robotic manipulation experiments, L2E exhibits increased performance when compared to pure RL, pure planning, or baseline methods combining learning and planning. © 2021, CC BY.

关键词： Robot programming

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Accurate Kinematic Modeling using Autoencoders on Differentiable Joints

Accurate Kinematic Modeling using Autoencoders on Differenti...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nikolas Wilhelm Sami Haddadin Rainer Burgkart Patrick Van Der Smagt Maximilian Karl Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Department of Orthopedics and Sports Orthopedics Klinikum Rechts der Isar School of Medicine Munich Germany Volkswagen Group Machine Learning Research Lab Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In robotics and biomechanics, accurately determining joint parameters and computing the corresponding forward and inverse kinematics are critical yet often challenging tasks, especially when dealing with highly individualized and partly unknown systems. This paper unveils a cutting-edge kinematic optimizer, underpinned by an autoencoder-based architecture, to address these challenges. Utilizing a neural network, our approach simulates inverse kinematics, converting measurement data into joint-specific parameters during encoding, enabling a stable optimization process. These parameters are subsequently processed through a predefined, differentiable forward kinematics model, resulting in a decoded representation of the original data. Beyond offering a comprehensive solution to kinematics challenges, our method also unveils previously unidentified joint parameters. Real experimental data from knee and hand joints validate the optimizer’s efficacy. Additionally, our optimizer is multifunctional: it streamlines the modeling and automation of kinematics and enables a nuanced evaluation of diverse modeling techniques. By assessing the differences in reconstruction losses, we illuminate the merits of each approach. Collectively, this preliminary study signifies advancements in kinematic optimization, with potential applications spanning both biomechanics and robotics.

关键词： Biomechanics Automation Biological system modeling Neural networks Kinematics Computer architecture Encoding

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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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Design and Implementation of a Robotic Testbench for Analyzing Pincer Grip Execution in Human Specimen Hands

Design and Implementation of a Robotic Testbench for Analyzi...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Nikolas Wilhelm Claudio Glowalla Sami Haddadin Julian Schote Hannes Höppner Patrick van der Smagt Maximilian Karl Rainer Burgkart Department of Orthopedics and Sports Orthopedics Klinikum Rechts der Isar School of Medicine Munich Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Humanoid Robotics Laboratory Berliner Hochschule für Technik Berlin Germany Machine Learning Research Lab Volkswagen Group Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This study presents an innovative test rig engineered to explore the kinematic and viscoelastic characteristics of human specimen hands. The rig features eight force-controlled motors linked to muscle tendons, enabling precise stimulation of hand specimens. Hand movements are monitored through an optical tracking system, while a force-torque sensor quantifies the resultant fingertip loads. Employing this setup, we successfully demonstrated a pincer grip using a cadaver hand and measured both muscle forces and grip strength. Our results reveal a nonlinear relationship between tendon forces and grip strength, which can be modeled by an exponential fit. This investigation serves as a nexus between biomechanical and robotics-focused research, providing critical insights for the advancement of robotic hand actuation and therapeutic interventions.

关键词： Biomechanics Technological innovation Tracking Stimulated emission Biological system modeling Muscles Motors

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