Autonomous Golf Putting with Data-Driven and Physics-Based Methods

• URL: http://arxiv.org/abs/2211.08081v1
• Date: Tue, 15 Nov 2022 12:05:03 GMT
• Title: Autonomous Golf Putting with Data-Driven and Physics-Based Methods
• Authors: Annika Junker, Niklas Fittkau, Julia Timmermann, Ansgar Tr\"achtler
• Abstract summary: We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods. Apart from the mechatronic control design of the robot, this task is accomplished by a camera system with image recognition and a neural network. We demonstrate the synergetic combination of data-driven and physics-based methods on the golf robot as a mechatronic example system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitrary point on the green. Apart from the mechatronic control design of the robot, this task is accomplished by a camera system with image recognition and a neural network for predicting the stroke velocity vector required for a successful hole-in-one. To minimize the number of time-consuming interactions with the real system, the neural network is pretrained by evaluating basic physical laws on a model, which approximates the golf ball dynamics on the green surface in a data-driven manner. Thus, we demonstrate the synergetic combination of data-driven and physics-based methods on the golf robot as a mechatronic example system.

Related papers

• Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
  Differentiable simulation has become a powerful tool for system identification. Our approach calibrates object properties by using information from the robot, without relying on data from the object itself. We demonstrate the effectiveness of our method on a low-cost robotic platform.
  arXiv  Detail & Related papers  (2024-10-04T20:48:38Z)
• RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing [38.97168020979433]
  We introduce an approach that combines visual and tactile sensing for robotic manipulation by learning a neural, tactile-informed dynamics model. Our proposed framework, RoboPack, employs a recurrent graph neural network to estimate object states. We demonstrate our approach on a real robot equipped with a compliant Soft-Bubble tactile sensor on non-prehensile manipulation and dense packing tasks.
  arXiv  Detail & Related papers  (2024-07-01T16:08:37Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning [6.229216953398305]
  A robot self-model is a representation of the robot's physical morphology that can be used for motion planning tasks. We propose a new encoder-based neural density field architecture for dynamic object-centric scenes conditioned on high numbers of degrees of freedom. In a 7-DOF robot test setup, the learned self-model achieves a Chamfer-L2 distance of 2% of the robot's dimension workspace.
  arXiv  Detail & Related papers  (2023-10-05T16:01:29Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks [32.00371492516123]
  We present a model-based planning framework for modeling and manipulating elasto-plastic objects. Our system, RoboCraft, learns a particle-based dynamics model using graph neural networks (GNNs) to capture the structure of the underlying system. We show through experiments that with just 10 minutes of real-world robotic interaction data, our robot can learn a dynamics model that can be used to synthesize control signals to deform elasto-plastic objects into various target shapes.
  arXiv  Detail & Related papers  (2022-05-05T20:28:15Z)
• In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning [8.365690203298966]
  We report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. A manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. We constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor.
  arXiv  Detail & Related papers  (2021-03-17T02:11:58Z)
• Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots [55.056709056795206]
  Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot.
  arXiv  Detail & Related papers  (2021-02-05T03:50:54Z)
• An analytical diabolo model for robotic learning and control [15.64227695210532]
  We derive an analytical model of the diabolo-string system and compare its accuracy using data recorded via motion capture. We show that our model outperforms a deep-learning-based predictor, both in terms of precision and physically consistent behavior. We test our method on a real robot system by playing the diabolo, and throwing it to and catching it from a human player.
  arXiv  Detail & Related papers  (2020-11-18T03:38:12Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.