Proprioceptive-only State Estimation for Legged Robots with Set-Coverage Measurements of Learned Dynamics

• URL: http://arxiv.org/abs/2603.18308v1
• Date: Wed, 18 Mar 2026 21:48:18 GMT
• Title: Proprioceptive-only State Estimation for Legged Robots with Set-Coverage Measurements of Learned Dynamics
• Abstract summary: Proprioceptive-only state estimation is attractive for legged robots since it is computationally cheaper and is unaffected by perceptually degraded conditions.<n>Recent approaches produce these estimates with learned measurement models and fuse with IMU data, under a Gaussian noise assumption.<n>We propose a framework that characterizes the measurement noise using set-coverage statements that do not assume any distribution.
• Score: 7.713910967779935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Proprioceptive-only state estimation is attractive for legged robots since it is computationally cheaper and is unaffected by perceptually degraded conditions. The history of joint-level measurements contains rich information that can be used to infer the dynamics of the system and subsequently produce navigational measurements. Recent approaches produce these estimates with learned measurement models and fuse with IMU data, under a Gaussian noise assumption. However, this assumption can easily break down with limited training data and render the estimates inconsistent and potentially divergent. In this work, we propose a proprioceptive-only state estimation framework for legged robots that characterizes the measurement noise using set-coverage statements that do not assume any distribution. We develop a practical and computationally inexpensive method to use these set-coverage measurements with a Gaussian filter in a systematic way. We validate the approach in both simulation and two real-world quadrupedal datasets. Comparison with the Gaussian baselines shows that our proposed method remains consistent and is not prone to drift under real noise scenarios.

Related papers

• Local Conformal Calibration of Dynamics Uncertainty from Semantic Images [11.10792616622245]
  We introduce Observation-aware Conformal Uncertainty Local-Calibration (OCULAR)<n>OCULAR is a conformal prediction-based algorithm that uses perception information to provide uncertainty quantification guarantees.<n>We validate our algorithm on a double-integrator system subject to random perturbations and significant model mismatch.
  arXiv  Detail & Related papers  (2026-05-13T05:33:42Z)
• Convolutional Maximum Mean Discrepancy for Inference in Noisy Data [10.176012052682072]
  We introduce a novel framework for inference with samples corrupted by potentially heteroscedastic noise from a known distribution.<n>We establish finite-sample deviation bounds that are unaffected by measurement error and prove an equivalence between testing under noise and kernel smoothing.<n>We demonstrate the practical effectiveness of our approach through simulations and applications in astronomy and social sciences.
  arXiv  Detail & Related papers  (2026-04-13T20:06:41Z)
• Denoising Particle Filters: Learning State Estimation with Single-Step Objectives [2.320417845168326]
  We propose a novel particle filtering algorithm in which models are trained from individual state transitions.<n>We evaluate the proposed method on challenging robotic state estimation tasks in simulation.
  arXiv  Detail & Related papers  (2026-02-23T09:53:23Z)
• Robust Filtering -- Novel Statistical Learning and Inference Algorithms with Applications [0.0]
  State estimation serves as a fundamental task to enable intelligent decision-making in applications such as autonomous vehicles, robotics, healthcare monitoring, smart grids, intelligent transportation, and predictive maintenance.<n>Standard filtering assumes prior knowledge of noise statistics to extract latent system states from noisy sensor data.<n>Real-world scenarios involve abnormalities like outliers, biases, drifts, and missing observations with unknown or partially known statistics, limiting conventional approaches.<n>This thesis presents novel robust nonlinear filtering methods to mitigate these challenges.
  arXiv  Detail & Related papers  (2025-06-13T07:30:35Z)
• DynaNoise: Dynamic Probabilistic Noise Injection for Defending Against Membership Inference Attacks [6.610581923321801]
  Membership Inference Attacks (MIAs) pose a significant risk to the privacy of training datasets.<n>Traditional mitigation techniques rely on injecting a fixed amount of noise during training or inference.<n>We present DynaNoise, an adaptive approach that dynamically modulates noise injection based on query sensitivity.
  arXiv  Detail & Related papers  (2025-05-19T17:07:00Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Data value estimation on private gradients [84.966853523107]
  For gradient-based machine learning (ML) methods, the de facto differential privacy technique is perturbing the gradients with random noise.<n>Data valuation attributes the ML performance to the training data and is widely used in privacy-aware applications that require enforcing DP.<n>We show that the answer is no with the default approach of injecting i.i.d.random noise to the gradients because the estimation uncertainty of the data value estimation paradoxically linearly scales with more estimation budget.<n>We propose to instead inject carefully correlated noise to provably remove the linear scaling of estimation uncertainty w.r.t.the budget.
  arXiv  Detail & Related papers  (2024-12-22T13:15:51Z)
• Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements [57.04370580292727]
  We consider data-driven Bayesian state estimation from compressed measurements.<n>The dimension of the temporal measurement vector is lower than that of the temporal state vector to be estimated.<n>The underlying dynamical model of the state's evolution is unknown for a'model-free process'
  arXiv  Detail & Related papers  (2024-07-10T05:03:48Z)
• Noise-mitigated randomized measurements and self-calibrating shadow estimation [0.0]
  We introduce an error-mitigated method of randomized measurements, giving rise to a robust shadow estimation procedure. On the practical side, we show that error mitigation and shadow estimation can be carried out using the same session of quantum experiments.
  arXiv  Detail & Related papers  (2024-03-07T18:53:56Z)
• Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data [2.3018169548556977]
  We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, quantifies uncertainty in the parameters estimated but also is more robust when learning the correct model from limited and noisy data.
  arXiv  Detail & Related papers  (2024-02-23T14:41:35Z)
• A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study [68.8204255655161]
  We investigate and compare the performance of several local and global smoothing techniques to a priori denoise the state measurements. We show that, in general, global methods, which use the entire measurement data set, outperform local methods, which employ a neighboring data subset around a local point.
  arXiv  Detail & Related papers  (2022-01-29T23:31:25Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• TraDE: Transformers for Density Estimation [101.20137732920718]
  TraDE is a self-attention-based architecture for auto-regressive density estimation. We present a suite of tasks such as regression using generated samples, out-of-distribution detection, and robustness to noise in the training data.
  arXiv  Detail & Related papers  (2020-04-06T07:32:51Z)

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.