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learning and Adaptation of Inverse Dynamics Models: A Comparison

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Learning and Adaptation of Inverse Dynamics Models: A Compar...

IEEE-RAS International Conference on Humanoid Robots

作者： Kevin Hitzler Franziska Meier Stefan Schaal Tamim Asfour Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Max-Planck-Institute of Intelligent Systems Germany and Computational Learning and Motor Control Lab University of Southern California USA

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

Performing tasks with high accuracy while interacting with the real world requires a robot to have an exact representation of its inverse dynamics that can be adapted to new situations. In the past, various methods for learning inverse dynamics models have been proposed that combine the well-known rigid body dynamics with model-based parameter estimation, or learn directly on measured data using regression. However, there are still open questions regarding the efficiency of model-based learning compared to data-driven approaches as well as their capabilities to adapt to changing dynamics. In this paper, we compare the state-of-the-art inertial parameter estimation to a purely data-driven and a model-based approach on simulated and real data, collected with the humanoid robot Apollo. We further compare the adaptation capabilities of two models in a pick and place scenario while a) learning the model incrementally and b) extending the initially learned model with an error model. Based on this, we show the gap between simulation and reality and verify the importance of modeling nonlinear effects using regression. Furthermore, we demonstrate that error models outperform incremental learning regarding adaptation of inverse dynamics models.

关键词： Adaptation models Robots Mathematical model Inverse problems Dynamics Data models Parameter estimation

Hierarchical human-motion prediction and logic-geometric programming for minimal interference human-robot tasks

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arXiv 2021年

作者： Le, An T. Kratzer, Philipp Hagenmayer, Simon Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen/Stuttgart Germany Technische Universität Berlin TUB Germany

In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we propose a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP) [1]. Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. We assess the efficacy of the approach by training the prediction algorithms and testing the framework on the publicly available MoGaze dataset [2]. © 2021, CC BY-NC-ND.

关键词： Computer circuits

AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments

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arXiv 2023年

作者： Adajania, Vivek K. Zhou, Siqi Singh, Arun Kumar Schoellig, Angela P. Learning Systems and Robotics Lab Germany University of Toronto Institute for Aerospace Studies Canada Technical University of Munich Germany Vector Institute for Artificial Intelligence Canada University of Tartu Estonia

This paper presents a scalable online algorithm to generate safe and kinematically feasible trajectories for quadrotor swarms. Existing approaches rely on linearizing Euclidean distance-based collision constraints and on axis-wise decoupling of kinematic constraints to reduce the trajectory optimization problem for each quadrotor to a quadratic program (QP). This conservative approximation often fails to find a solution in cluttered environments. We present a novel alternative that handles collision constraints without linearization and kinematic constraints in their quadratic form while still retaining the QP form. We achieve this by reformulating the constraints in a polar form and applying an Alternating Minimization algorithm to the resulting problem. Through extensive simulation results, we demonstrate that, as compared to Sequential Convex Programming (SCP) baselines, our approach achieves on average a 72% improvement in success rate, a 36% reduction in mission time, and a 42 times faster per-agent computation time. We also show that collision constraints derived from discrete-time barrier functions (BF) can be incorporated, leading to different safety behaviours without significant computational overhead. Moreover, our optimizer outperforms the state-of-the-art optimal control solver ACADO in handling BF constraints with a 31 times faster per-agent computation time and a 44% reduction in mission time on average. We experimentally validated our approach on a Crazyflie quadrotor swarm of up to 12 quadrotors. The code with supplementary material and video are released for reference. © 2023, CC BY.

关键词： Motion planning

GraspME - Grasp manifold estimator

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arXiv 2021年

作者： Hager, Janik Bauer, Ruben Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab IPVS University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen/Stuttgart Germany Technische Universität Berlin TUB Germany

In this paper, we introduce a Grasp Manifold Estimator (GraspME) to detect grasp affordances for objects directly in 2D camera images. To perform manipulation tasks autonomously it is crucial for robots to have such graspability models of the surrounding objects. Grasp manifolds have the advantage of providing continuously infinitely many grasps, which is not the case when using other grasp representations such as predefined grasp points. For instance, this property can be leveraged in motion optimization to define goal sets as implicit surface constraints in the robot configuration space. In this work, we restrict ourselves to the case of estimating possible end-effector positions directly from 2D camera images. To this extend, we define grasp manifolds via a set of keypoints and locate them in images using a Mask R-CNN [1] backbone. Using learned features allows to generalize to different view angle, with potentially noisy images, and objects that were not part of the training set. We rely on simulation data only and perform experiments on simple and complex objects, including unseen ones. Our framework achieves an inference speed of 11.5 fps on a GPU, an average precision for keypoint estimation of 94.5% and a mean pixel distance of only 1.29. This shows that we can estimate the objects very well via bounding boxes and segmentation masks as well as approximate the correct grasp manifold's keypoint coordinates. © 2021, CC BY-NC-ND.

关键词： Cameras

A system for traded control teleoperation of manipulation tasks using intent prediction from hand gestures

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arXiv 2021年

作者： Oh, Yoojin Schäfer, Tim Rüther, Benedikt Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab IPVS University of Stuttgart Germany Max Planck Institute for Intelligent Systems MPI-IS Tübingen/Stuttgart Germany Technische Universität Berlin TUB Germany

This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp. This architecture allows the approach to rely on traded control instead of direct control: we use hand gestures to specify the goal objects for a sequential manipulation task, the robot then autonomously generates a grasping or a retrieving motion using trajectory optimization. The perception module relies on the model-based tracker to precisely track the 6D pose of the objects and makes use of a state of the art learning-based object detection and segmentation method, to initialize the tracker by automatically detecting objects in the scene. Goal objects are identified from user hand gestures using a trained a multi-layer perceptron classifier. After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i.e., one that does not use prediction) which shows that using intent prediction allows to bring down the overall task execution time. © 2021, CC BY-NC-ND.

关键词： Forecasting

Simulation and HRI recent perspectives with the MORSE simulator

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8810卷 13-24页

作者： Lemaignan, Séverin Hanheide, Marc Karg, Michael Khambhaita, Harmish Kunze, Lars Lier, Florian Lütkebohle, Ingo Milliez, Grégoire CHILI Lab. EPFL Lausanne Switzerland Centre for Autonomous Systems University of Lincoln United Kingdom IAS Technische Universität München Germany LAAS/CNRS Université de Toulouse France Intelligent Robotics Lab. University of Birmingham United Kingdom CITEC Bielefeld University Germany Machine Learning and Robotics Lab. Universität Stuttgart Germany

Simulation in robotics is often a love-hate relationship: while simulators do save us a lot of time and effort compared to regular deployment of complex software architectures on complex hardware, simulators are also known to evade many of the real issues that robots need to manage when they enter the real world. Because humans are the paragon of dynamic, unpredictable, complex, real world entities, simulation of human-robot interactions may look condemn to fail, or, in the best case, to be mostly useless. This collective article reports on five independent applications of the MORSE simulator in the field of human-robot interaction: It appears that simulation is already useful, if not essential, to successfully carry out research in the field of HRI, and sometimes in scenarios we do not anticipate. © 2014 Springer International Publishing Switzerland.

关键词： Simulators

Deep workpiece region segmentation for bin picking

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arXiv 2019年

作者： Khalid, Muhammad Usman Hager, Janik M. Kraus, Werner Huber, Marco F. Toussaint, Marc Robot and Assistive Systems Fraunhofer IPA Stuttgart Germany Machine Learning & Robotics Lab University of Stuttgart Germany Fraunhofer IPA Stuttgart and Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor. Distinguishing flat workpieces from bottom of the bin in point cloud imposes challenges in the localization of workpieces that lead to wrong or phantom detections. In this paper, we propose a framework that solves this problem by automatically segmenting workpiece regions from non-workpiece regions in a point cloud data. It is done in real time by applying a fully convolutional neural network trained on both simulated and real data. The real data has been labelled by our novel technique which automatically generates ground truth labels for real point clouds. Along with real time workpiece segmentation, our framework also helps in improving the number of detected workpieces and estimating the correct object poses. Moreover, it decreases the computation time by approximately 1s due to a reduction of the search space for the object pose estimation. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical detection

Probabilistic Spiking Neural Network for Robotic Tactile Continual learning

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Probabilistic Spiking Neural Network for Robotic Tactile Con...

IEEE International Conference on robotics and Automation (ICRA)

作者： Senlin Fang Yiwen Liu Chengliang Liu Jingnan Wang Yuanzhe Su Yupo Zhang Hoiio Kong Zhengkun Yi Xinyu Wu City University of Macau Macau China The Department of Intelligent Systems and Robot Learning Lab ISRL Group SIAT Branch Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology Shenzhen

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.

关键词： Continuing education Measurement Uncertainty Spiking neural networks learning (artificial intelligence) Benchmark testing Robot sensing systems

Deep, robust and single shot 3D multi-person human pose estimation in complex images

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arXiv 2019年

作者： Benzine, Abdallah Luvison, Bertrand Pham, Quoc Cuong Achard, Catherine CEA LIST Vision and Learning Lab for Scene Analysis PC 184 Gif-sur-YvetteF-91191 France Sorbonne University CNRS Institute for Intelligent Systems and Robotics ISIR ParisF-75005 France

In this paper, we propose a new single shot method for multi-person 3D human pose estimation in complex images. The model jointly learns to locate the human joints in the image, to estimate their 3D coordinates and to group these predictions into full human skeletons. The proposed method deals with a variable number of people and does not need bounding boxes to estimate the 3D poses. It leverages and extends the Stacked Hourglass Network and its multi-scale feature learning to manage multi-person situations. Thus, we exploit a robust 3D human pose formulation to fully describe several 3D human poses even in case of strong occlusions or crops. Then, joint grouping and human pose estimation for an arbitrary number of people are performed using the associative embedding method. Our approach significantly outperforms the state of the art on the challenging CMU Panoptic. Furthermore, it leads to good results on the complex and synthetic images from the newly proposed JTA Dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning