QuietPaw: Learning Quadrupedal Locomotion with Versatile Noise Preference Alignment

• URL: http://arxiv.org/abs/2503.05035v1
• Date: Thu, 06 Mar 2025 23:20:04 GMT
• Title: QuietPaw: Learning Quadrupedal Locomotion with Versatile Noise Preference Alignment
• Abstract summary: Noise from quadrupedal robots can disrupt human-centered environments like homes, offices, and hospitals.<n>We propose QuietPaw, a constrained learning-based algorithm that enables flexible, noise-aware locomotion by conditioning policy behavior on noise-reduction levels.<n>We validate our approach in simulation and the real world, demonstrating that CNCP can effectively balance locomotion performance and noise constraints.
• Score: 32.730540558720314
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: When operating at their full capacity, quadrupedal robots can produce loud footstep noise, which can be disruptive in human-centered environments like homes, offices, and hospitals. As a result, balancing locomotion performance with noise constraints is crucial for the successful real-world deployment of quadrupedal robots. However, achieving adaptive noise control is challenging due to (a) the trade-off between agility and noise minimization, (b) the need for generalization across diverse deployment conditions, and (c) the difficulty of effectively adjusting policies based on noise requirements. We propose QuietPaw, a framework incorporating our Conditional Noise-Constrained Policy (CNCP), a constrained learning-based algorithm that enables flexible, noise-aware locomotion by conditioning policy behavior on noise-reduction levels. We leverage value representation decomposition in the critics, disentangling state representations from condition-dependent representations and this allows a single versatile policy to generalize across noise levels without retraining while improving the Pareto trade-off between agility and noise reduction. We validate our approach in simulation and the real world, demonstrating that CNCP can effectively balance locomotion performance and noise constraints, achieving continuously adjustable noise reduction.

Related papers

• Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness [0.5188499345298442]
  Deep learning is highly sensitive to the nature of training noise, especially under out-of-distribution conditions.<n>This study investigates the influence of noise parameters, such as type, scale, and complexity on the performance, generalization and robustness of neural networks.
  arXiv  Detail & Related papers  (2026-09-13T13:30:37Z)
• ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising [1.539942973115038]
  deep learning-based denoising has become widely adopted in low-dose CT.<n>Conventional models fail to account for non-stationary and spatially correlated CT noise characteristics.<n>We propose an Efficient Noise COntext-aware REpresentation framework.
  arXiv  Detail & Related papers  (2026-08-11T00:57:39Z)
• Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments [68.02342920827135]
  We propose NoisyAgent, an agentic training framework that explicitly incorporates environmental imperfections into the agent learning process.<n>We identify two major sources of interaction noise in real-world scenarios: user noise, which captures ambiguity and variability in user interaction, and tool noise, which reflects failures and anomalies in tool execution.<n>Extensive experiments demonstrate that our approach consistently improves agent robustness under noisy and dynamic environments.
  arXiv  Detail & Related papers  (2026-05-26T16:02:00Z)
• Unified Noise Steering for Efficient Human-Guided VLA Adaptation [28.625432744456763]
  Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation.<n>Noise-space RL lowers the cost by keeping the pretrained VLA fixed as a denoising generator while updating only a lightweight actor that predicts the noise.<n>We propose UniSteer, a Unified Noise Steering framework that combines human corrective guidance with noise-space RL through approximate action-to-noise inversion.
  arXiv  Detail & Related papers  (2026-05-11T16:37:34Z)
• Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization [32.04001045559129]
  We present a training-free diffusion noise optimization framework for generating motion that satisfies customized zero-temporal goal functions.<n>This framework is used to enable applications such as controllable character animation and behavior synthesis for virtual agents.
  arXiv  Detail & Related papers  (2026-05-08T17:43:29Z)
• Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification [55.56234913868664]
  We propose Test-time Adaptive Hierarchical Co-enhanced Denoising Network (TAHCD) for reliable learning on multimodal data.<n>The proposed method achieves superior classification performance, robustness, and generalization compared with state-of-the-art reliable multimodal learning approaches.
  arXiv  Detail & Related papers  (2026-01-12T03:14:12Z)
• Real Noise Decoupling for Hyperspectral Image Denoising [14.247569090609828]
  Hyperspectral image (HSI) denoising is a crucial step in enhancing the quality of HSIs.<n>Noise modeling methods can fit noise distributions to generate synthetic HSIs to train denoising networks.<n>We propose a multi-stage noise-decoupling framework that decomposes complex noise into explicitly modeled and implicitly modeled components.
  arXiv  Detail & Related papers  (2025-11-21T12:23:07Z)
• Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance [54.88271057438763]
  Noise Awareness Guidance (NAG) is a correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule.<n>NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.
  arXiv  Detail & Related papers  (2025-10-14T13:31:34Z)
• Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations [5.975221928631025]
  Noisy Training is one of the most effective techniques for enhancing robustness, but its performance degrades in real-world environments. We propose Variance-Aware Noisy Training, a novel approach that incorporates noise schedules which emulate the evolving noise conditions encountered during inference. We demonstrate a significant increase in robustness, from 72.3% with conventional Noisy Training to 97.3% with Variance-Aware Noisy Training on CIFAR-10 and from 38.5% to 89.9% on Tiny ImageNet.
  arXiv  Detail & Related papers  (2025-03-20T14:34:03Z)
• Learning Degradation-Independent Representations for Camera ISP Pipelines [14.195578257521934]
  We propose a novel approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and it outperforms state-of-the-art methods across various downstream tasks.
  arXiv  Detail & Related papers  (2023-07-03T05:38:28Z)
• Realistic Noise Synthesis with Diffusion Models [44.404059914652194]
  Deep denoising models require extensive real-world training data, which is challenging to acquire.<n>We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise [25.51080528855037]
  Ego-noise is mainly caused by the internal motors and joints of a robot. We propose a multichannel partially adaptive scheme to jointly model ego-noise and environmental noise.
  arXiv  Detail & Related papers  (2023-03-27T09:40:14Z)
• Latent Class-Conditional Noise Model [54.56899309997246]
  We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
  arXiv  Detail & Related papers  (2023-02-19T15:24:37Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• NLIP: Noise-robust Language-Image Pre-training [95.13287735264937]
  We propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. Our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way.
  arXiv  Detail & Related papers  (2022-12-14T08:19:30Z)
• C2N: Practical Generative Noise Modeling for Real-World Denoising [53.96391787869974]
  We introduce a Clean-to-Noisy image generation framework, namely C2N, to imitate complex real-world noise without using paired examples. We construct the noise generator in C2N accordingly with each component of real-world noise characteristics to express a wide range of noise accurately.
  arXiv  Detail & Related papers  (2022-02-19T05:53:46Z)
• Towards Adversarially Robust Deep Image Denoising [199.2458715635285]
  This work systematically investigates the adversarial robustness of deep image denoisers (DIDs) We propose a novel adversarial attack, namely Observation-based Zero-mean Attack (sc ObsAtk) to craft adversarial zero-mean perturbations on given noisy images. To robustify DIDs, we propose hybrid adversarial training (sc HAT) that jointly trains DIDs with adversarial and non-adversarial noisy data.
  arXiv  Detail & Related papers  (2022-01-12T10:23:14Z)
• Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction [109.44933866397123]
  Noise robustness is essential for deploying automatic speech recognition systems in real-world environments. We employ a noise-robust representation learned by a refined self-supervised framework for noisy speech recognition. We achieve comparable performance to the best supervised approach reported with only 16% of labeled data.
  arXiv  Detail & Related papers  (2021-10-28T20:39:02Z)
• Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training [16.807298318504156]
  We show that we can improve performance by dynamically routing samples to specialized feature encoders for each noise condition. We extend these improvements to the multimodal setting by dynamically routing samples to maintain temporal ordering.
  arXiv  Detail & Related papers  (2020-10-21T18:07:32Z)

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.