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Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

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arXiv 2022年

作者： Lee, Jee-Eun Lee, Jaemin Bandyopadhyay, Tirthankar Sentis, Luis Human Centered Robotics Laboratory Department of Aerospace Engineering and Engineering Mechanics The University of Texas Austin United States The Department of Mechanical and Civil Engineering California Institute of Technology PasadenaCA United States Faculty of Department of Aerospace Engineering and Engineering Mechanics The University of Texas Austin United States Robotics and Autonomous Systems Group Data61 Csiro QLD 4069 Australia

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Existing frameworks that represent all data in vector space fail to consider the structured information of the robot, such as leg symmetry, rotational symmetry, and physics invariance. As a result, these schemes require vast amounts of training data to learn the system's redundant elements because they are learned independently. Instead, we propose considering the geometric prior by representing the system in symmetrical object groups and designing neural network architecture to assess invariance and equivariance between the objects. Finally, we demonstrate the effectiveness of our approach by comparing the generalization to unseen data of the proposed model and the existing models. We also implement a controller of a climbing robot based on learned inverse dynamics models. The results show that our method generates accurate control inputs that help the robot reach the desired state while requiring less training data than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Vector spaces

Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility

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Online 3D Frontier-Based UGV and UAV Exploration Using Direc...

International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Jason Williams Shu Jiang Matthew O’Brien Glenn Wagner Emili Hernandez Mark Cox Alex Pitt Ron Arkin Nicolas Hudson CSIRO Data61 Robotics and Autonomous Systems Group Georgia Tech Mobile Robot Laboratory Emesent

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

While robots have long been proposed as a tool to reduce human personnel's exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.

关键词： Exploration Robot Environments Three Dimensions drone honey bees Unmanned Aerial Vehicle subsurface environment Scalability underground Danger

Locus: LiDAR-based place recognition using spatiotemporal higher-order pooling

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arXiv 2020年

作者： Vidanapathirana, Kavisha Moghadam, Peyman Harwood, Ben Zhao, Muming Sridharan, Sridha Fookes, Clinton Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Brisbane Australia

— Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a method for extracting and encoding topological and temporal information related to components in a scene and demonstrate how the inclusion of this auxiliary information in place description leads to more robust and discriminative scene representations. Second-order pooling along with a nonlinear transform is used to aggregate these multi-level features to generate a fixed-length global descriptor, which is invariant to the permutation of input features. The proposed method outperforms state-of-the-art methods on the KITTI dataset. Furthermore, Locus is demonstrated to be robust across several challenging situations such as occlusions and viewpoint changes in 3D LiDAR point clouds. The open-source implementation is available at: https://***/csiro-robotics/locus . Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

PaintPath: Defining Path Directionality in Maps for autonomous Ground Vehicles

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PaintPath: Defining Path Directionality in Maps for Autonomo...

2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Riley Bowyer Thomas Lowe Paulo Borges Tirthankar Bandyopadhyay Tobias Löw David Haddon Robotics and Autonomous Systems Group Data61 CSIRO Brisbane Australia Multi-Scale Robotics Lab ETH Zürich Zurich Switzerland

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Directionality in path planning is essential for efficient autonomous navigation in a number of real-world environments. In many map-based navigation scenarios, the viable path from a given point A to point B is not the same as the viable path from B to A. We present a method that automatically incorporates preferred navigation directionality into a path planning costmap. This `preference' is represented by coloured paths in the costmap. The colourisation is obtained based on an analysis of the driving trajectory generated by the robot as it navigates through the environment. Hence, our method augments this driving trajectory by intelligently colouring it according to the orientation of the robot during the run. Creating an analogy between the vehicle orientation angle and the hue angle in the Hue-Saturation-Value colour space, the method uses the hue, saturation and value components to encode the direction, directionality and scalar cost, respectively, into a costmap image. We describe a costing function to be used by the A* algorithm to incorporate this information to plan direction-aware vehicle paths. Our experiments with LiDAR-based localisation and autonomous driving in real environments illustrate the applicability of the method.

关键词： Space vehicles Navigation Costing Image color analysis Trajectory Planning autonomous robots

Elasticity meets continuous-time: map-centric dense 3D LiDAR slam

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arXiv 2020年

作者： Park, Chanoh Moghadam, Peyman Williams, Jason Kim, Soohwan Sridharan, Sridha Fookes, Clinton Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Brisbane Australia Division of Smart Automotive Engineering Sun Moon University Korea Republic of

Map-centric SLAM utilizes elasticity as a means of loop closure. This approach reduces the cost of loop closure while still provides large-scale fusion-based dense maps, when compared to the trajectory-centric SLAM approaches. In this paper, we present a novel framework for 3D LiDAR based map-centric SLAM. Having the advantages of a map-centric approach, our method exhibits new features to overcome the shortcomings of existing systems, associated with multi-modal sensor fusion and LiDAR motion distortion. This is accomplished through the use of a local Continuous-Time (CT) trajectory representation. Also, our surface resolution preservative matching algorithm and Wishart-based surfel fusion model enables non-redundant yet dense mapping. Furthermore, we present a robust metric loop closure model to make the approach stable regardless of where the loop closure occurs. Finally, we demonstrate our approach through both simulation and real data experiments using multiple sensor payload configurations and environments to illustrate its utility and robustness. Copyright © 2020, The Authors. All rights reserved.

关键词： Elasticity

Canopy density estimation in perennial horticulture crops using 3D spinning LiDAR SLAM

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arXiv 2020年

作者： Lowe, Thomas Moghadam, Peyman Edwards, Everard Williams, Jason Robotics and Autonomous Systems CSIRO DATA61 BrisbaneQLD4069 Australia CSIRO Agriculture and Food Waite Campus SA5064 Australia

We propose a 3D ray cloud based method for estimating the canopy density of vineyards and orchards that have been scanned by a vehicle-mounted mobile 3D spinning LiDAR (AgScan3D). The method is composed of two parts. Firstly, the AgScan3D data is processed through a Continuous-Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Secondly, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analysed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons, resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of vines with a combined total of approximately 93000 of scanned vines. The field datasets and ray cloud library will be released publicly. Copyright © 2020, The Authors. All rights reserved.

关键词： Continuous time systems

POMDP-based candy server: Lessons learned from a seven day demo 29

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POMDP-based candy server: Lessons learned from a seven day d...

29th International Conference on Automated Planning and Scheduling, ICAPS 2019

作者： Hoerger, Marcus Song, Joshua Kurniawati, Hanna Elfes, Alberto School of Information Technology and Electrical Engineering University of Queensland Australia Research School of Computer Science Australian National University Australia Robotics and Autonomous Systems Group Data61 CSIRO

ISBN: (纸本)9781577358077

An autonomous robot must decide a good strategy to achieve its long term goal, despite various types of uncertainty. The Partially Observable Markov Decision Processes (POMDPs) is a principled framework to address such a decision making problem. Despite the computational intractability of solving POMDPs, the past decade has seen substantial advancement in POMDP solvers. This paper presents our experience in enabling on-line POMDP solving to become the sole motion planner for a robot manipulation demo at IEEE SIMPAR and ICRA 2018. The demo scenario is a candy-serving robot: A 6-DOFs robot arm must pick-up a cup placed on a table by a user, use the cup to scoop candies from a box, and put the cup of candies back on the table. The average perception error is ∼3cm (≈ the radius of the cup), affecting the position of the cup and the surface level of the candies. This paper presents a strategy to alleviate the curse of history issue plaguing this scenario, the perception system and its integration with the planner, and lessons learned in enabling an online POMDP solver to become the sole motion planner of this entire task. The POMDP-based system were tested through a 7 days live demo at the two conferences. In this demo, 150 runs were attempted and 98% of them were successful. We also conducted further experiments to test the capability of our POMDP-based system when the environment is relatively cluttered by obstacles and when the user moves the cup while the robot tries to pick it up. In both cases, our POMDP-based system reaches a success rate of 90% and above. © 2019 Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

Utilising Evolutionary Algorithms to Design Granular Materials for Industrial Applications

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Utilising Evolutionary Algorithms to Design Granular Materia...

International Conference on Machine Learning and Applications (ICMLA)

作者： Gary W. Delaney David Howard Krystal De Napoli Computational Modelling Group CSIRO Data61 Melbourne Australia Robotics and Autonomous Systems Group CSIRO Data61 Brisbane Australia

Granular materials, such as sands, soils, grains and powders, are ubiquitous in both natural and artificial systems. They are core to many industrial systems from mining and food production to pharmaceuticals and construction. Granular media display unique properties, including their ability to flow like a liquid at low densities and jam in to a solid state at high densities. Granular materials are used functionally in a number of industrial systems, where for example their insulating, energy absorption, filtration or vibration damping properties are variously exploited. A recent emerging industrial application is to utilise the jamming transition of granular matter (transition from a sold to a liquid) to create functional jammed systems such as universal grippers or soft robotic devices with potential broad impact across many industrial sectors. However, controlling the microscopic properties of such systems to elicit bespoke functional granular systems remains challenging due to the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Here, we investigate the use of evolution to explore the functional landscapes of granular systems. We employ a superellipsoid representation of the particle shape which allows us to smoothly transition between a large variety of particle aspect ratios and angularities, and investigate the use of multi-component systems alongside homogenous granular arrangements. Results show the ability to successfully characterise a sample design space, and represents an important step towards the creation of bespoke jammed systems with a range of practical applications across broad swathes of industry.

关键词： Shape Jamming Morphology Evolutionary computation Liquids Atmospheric measurements Particle measurements

Spatiotemporal camera-LiDAR calibration: A targetless and structureless approach

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arXiv 2020年

作者： Park, Chanoh Moghadam, Peyman Kim, Soohwan Sridharan, Sridha Fookes, Clinton Robotics and Autonomous Systems DATA61 CSIRO BrisbaneQLD4069 Australia Brisbane Australia Brisbane Australia Division of Smart Automotive Engineering Sun Moon University Korea Republic of

The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems. These systems also need to be spatially and temporally co-registered to be effective. In this paper, we propose a targetless and structureless spatiotemporal camera-LiDAR calibration method. Our method combines a closed-form solution with a modified structureless bundle adjustment where the coarse-to-fine approach does not require an initial guess on the spatiotemporal parameters. Also, as 3D features (structure) are calculated from triangulation only, there is no need to have a calibration target or to match 2D features with the 3D point cloud which provides flexibility in the calibration process and sensor configuration. We demonstrate the accuracy and robustness of the proposed method through both simulation and real data experiments using multiple sensor payload configurations mounted to hand-held, aerial and legged robot systems. Also, qualitative results are given in the form of a colorized point cloud visualization. Copyright © 2020, The Authors. All rights reserved.

关键词： Cameras