Learning Quadruped Walking from Seconds of Demonstration

• URL: http://arxiv.org/abs/2603.06961v1
• Date: Sat, 07 Mar 2026 00:47:30 GMT
• Title: Learning Quadruped Walking from Seconds of Demonstration
• Abstract summary: We give a principled analysis of why imitation learning with quadrupeds can be inherently effective in a small data regime.<n>A new imitation learning method regulates the alignment between variations in a latent space and those over the output actions.
• Score: 27.175937325259643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quadruped locomotion provides a natural setting for understanding when model-free learning can outperform model-based control design, by exploiting data patterns to bypass the difficulty of optimizing over discrete contacts and the combinatorial explosion of mode changes. We give a principled analysis of why imitation learning with quadrupeds can be inherently effective in a small data regime, based on the structure of its limit cycles, Poincaré return maps, and local numerical properties of neural networks. The understanding motivates a new imitation learning method that regulates the alignment between variations in a latent space and those over the output actions. Hardware experiments confirm that a few seconds of demonstration is sufficient to train various locomotion policies from scratch entirely offline with reasonable robustness.

Related papers

• Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning [65.85335291827086]
  This paper tries to learn and understand underlying semantic variations from distracting videos via offline-to-online latent distillation and flexible disentanglement constraints.<n>We pretrain the action-free video prediction model offline with disentanglement regularization to extract semantic knowledge from distracting videos.<n>For finetuning in the online environment, we exploit the knowledge from the pretrained model and introduce a disentanglement constraint to the world model.
  arXiv  Detail & Related papers  (2025-03-11T13:50:22Z)
• Offline Adaptation of Quadruped Locomotion using Diffusion Models [59.882275766745295]
  We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills.<n>We show that these capabilities are compatible with a multi-skill policy and can be applied with little modification and minimal compute overhead.<n>We verify the validity of our approach with hardware experiments on the ANYmal quadruped platform.
  arXiv  Detail & Related papers  (2024-11-13T18:12:15Z)
• Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
  In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
  arXiv  Detail & Related papers  (2024-10-31T22:54:34Z)
• 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.<n>We validate our approach through simulations with various planar soft manipulators.<n>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)
• Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks [62.48782506095565]
  We show that due to the greedy nature of learning in deep neural networks, models tend to rely on just one modality while under-fitting the other modalities. We propose an algorithm to balance the conditional learning speeds between modalities during training and demonstrate that it indeed addresses the issue of greedy learning.
  arXiv  Detail & Related papers  (2022-02-10T20:11:21Z)
• An Adaptable Approach to Learn Realistic Legged Locomotion without Examples [38.81854337592694]
  This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. We present experimental results showing that even in a model-free setup, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot.
  arXiv  Detail & Related papers  (2021-10-28T10:14:47Z)
• Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint [9.36739413306697]
  We discuss a methodology to learn differential equation(s) using noisy and sparsely sampled measurements. In our methodology, the main innovation can be seen in of integration of deep neural networks with a classical numerical integration method.
  arXiv  Detail & Related papers  (2021-09-23T15:43:45Z)
• STAR: Sparse Transformer-based Action Recognition [61.490243467748314]
  This work proposes a novel skeleton-based human action recognition model with sparse attention on the spatial dimension and segmented linear attention on the temporal dimension of data. Experiments show that our model can achieve comparable performance while utilizing much less trainable parameters and achieve high speed in training and inference.
  arXiv  Detail & Related papers  (2021-07-15T02:53:11Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Learning Unstable Dynamics with One Minute of Data: A Differentiation-based Gaussian Process Approach [47.045588297201434]
  We show how to exploit the differentiability of Gaussian processes to create a state-dependent linearized approximation of the true continuous dynamics. We validate our approach by iteratively learning the system dynamics of an unstable system such as a 9-D segway.
  arXiv  Detail & Related papers  (2021-03-08T05:08:47Z)

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.