Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

• URL: http://arxiv.org/abs/2102.12942v2
• Date: Mon, 1 Mar 2021 10:56:48 GMT
• Title: Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
• Authors: Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco and Carlo Ciliberto
• Abstract summary: We introduce a structured prediction algorithm that combines a data-driven strategy with a forward kinematics function. The proposed approach ensures that predicted joint configurations are well within the robot's constraints.
• Score: 39.513301957826435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function -- even when this function is misspeficied -- to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks.

Related papers

• Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control [2.058941610795796]
  This paper introduces a streamlined method for learning low-dimensional, physics-based models. We validate our approach through simulations with various planar soft manipulators. Thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
  arXiv  Detail & Related papers  (2024-10-31T18:37:22Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Towards Robust Dataset Learning [90.2590325441068]
  We propose a principled, tri-level optimization to formulate the robust dataset learning problem. Under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset.
  arXiv  Detail & Related papers  (2022-11-19T17:06:10Z)
• Sample Efficient Dynamics Learning for Symmetrical Legged Robots:Leveraging Physics Invariance and Geometric Symmetries [14.848950116410231]
  This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system. Existing frameworks that represent all data in vector space fail to consider the structured information of the robot.
  arXiv  Detail & Related papers  (2022-10-13T19:57:46Z)
• 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)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Goal Agnostic Planning using Maximum Likelihood Paths in Hypergraph World Models [1.370633147306388]
  We present a hypergraph-based machine learning algorithm, a datastructure--driven maintenance method, and a planning algorithm based on a probabilistic application of Dijkstra's algorithm. We prove that the algorithm determines optimal solutions within the problem space, mathematically bound learning performance, and supply a mathematical model analyzing system state progression through time.
  arXiv  Detail & Related papers  (2021-10-18T16:22:33Z)
• Machine Learning to Tackle the Challenges of Transient and Soft Errors in Complex Circuits [0.16311150636417257]
  Machine learning models are used to predict accurate per-instance Functional De-Rating data for the full list of circuit instances. The presented methodology is applied on a practical example and various machine learning models are evaluated and compared.
  arXiv  Detail & Related papers  (2020-02-18T18:38:54Z)
• On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques [0.16311150636417257]
  De-Rating or Vulnerability Factors are a major feature of failure analysis efforts mandated by today's Functional Safety requirements. New approach is proposed which uses Machine Learning to estimate the Functional De-Rating of individual flip-flops.
  arXiv  Detail & Related papers  (2020-02-18T15:18:31Z)

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.