A Foot Resistive Force Model for Legged Locomotion on Muddy Terrains

• URL: http://arxiv.org/abs/2604.12006v1
• Date: Mon, 13 Apr 2026 19:53:52 GMT
• Title: A Foot Resistive Force Model for Legged Locomotion on Muddy Terrains
• Abstract summary: Legged robots face significant challenges in moving and navigating on deformable and highly yielding terrain such as mud.<n>We present a resistive force model for legged foot-mud interactions.<n>We take advantage of the model to design a new morphing robotic foot for effective and efficient legged locomotion.
• Score: 13.833218559967348
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Legged robots face significant challenges in moving and navigating on deformable and highly yielding terrain such as mud. We present a resistive force model for legged foot-mud interactions. The model captures rheological behaviors such as visco-elasticity, thixotropy of the mud suspension and retractive suction. One attractive property of this new model lies in its effective, uniform formulation to provide underlying physical interpretation and accurate resistive force predictions. We further take advantage of the resistive force model to design a new morphing robotic foot for effective and efficient legged locomotion. We conduct extensive experiments to validate the force model, and the results demonstrate that the morphing foot enhances not only the locomotion mobility but also energy-efficiency of walking in mud. The new resistive force model can be further used to develop data-driven simulation and locomotion control of legged robots on muddy terrains.

Related papers

• Robust bipedal locomotion on flowable slopes via foot-driven terrain manipulation [9.438718890087712]
  Bipedal robots are challenging to control because they operate close to instability.<n>Small variations in foot-induced contact can rapidly destabilize locomotion.<n>We study how controlling terrain response can improve bipedal locomotion on granular slopes.
  arXiv  Detail & Related papers  (2026-07-13T17:42:03Z)
• Bipedal-Walking-Dynamics Model on Granular Terrains [14.386787371962987]
  Bipedal robots have demonstrated high agility and mobility in unstructured environments such as sand.<n>We present a new dynamics-modeling approach to capture and predict bipedal-walking locomotion on granular media.
  arXiv  Detail & Related papers  (2026-04-13T19:12:22Z)
• Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped [0.0]
  SLOT (Soft Legged Omnidirectional Tetrapod) is a tendon-driven soft quadruped robot with 3D-printed TPU legs.<n>Each leg is modeled as a deformable continuum using discrete Cosserat rod theory.<n>A modular whole-body modeling framework is introduced, in which compliant leg dynamics are represented.
  arXiv  Detail & Related papers  (2026-02-18T11:14:22Z)
• Skeleton2Stage: Reward-Guided Fine-Tuning for Physically Plausible Dance Generation [49.50118203284611]
  Motions that look plausible as joint trajectories often exhibit body self-penetration and Foot-Ground Contact (FGC) anomalies when visualized with a human body mesh.<n>We address this skeleton-to-mesh gap by deriving physics-based rewards from the body mesh.<n>Our method can significantly improve the physical plausibility of generated motions, yielding more realistic and aesthetically pleasing dances.
  arXiv  Detail & Related papers  (2026-02-14T13:48:13Z)
• A compliant ankle-actuated compass walker with triggering timing control [0.552480439325792]
  A new model is proposed, named triggering controlled ankle actuated gait compass (TC-AACG), which allows non-instantaneous compliant ankle pushoff.<n>Our systematic examination shows that the proposed approach extends the locomotion capabilities of a biped model compared to impulsive ankle pushoff approach.
  arXiv  Detail & Related papers  (2026-02-06T20:00:19Z)
• Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control [47.423243831156285]
  We present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control.<n>We demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data.<n>KINESIS generates muscle activity patterns that correlate well with human EMG activity.
  arXiv  Detail & Related papers  (2025-03-18T18:37:49Z)
• HUMOS: Human Motion Model Conditioned on Body Shape [54.20419874234214]
  We introduce a new approach to develop a generative motion model based on body shape.<n>We show that it's possible to train this model using unpaired data.<n>The resulting model generates diverse, physically plausible, and dynamically stable human motions.
  arXiv  Detail & Related papers  (2024-09-05T23:50:57Z)
• Exploring Model Transferability through the Lens of Potential Energy [78.60851825944212]
  Transfer learning has become crucial in computer vision tasks due to the vast availability of pre-trained deep learning models. Existing methods for measuring the transferability of pre-trained models rely on statistical correlations between encoded static features and task labels. We present an insightful physics-inspired approach named PED to address these challenges.
  arXiv  Detail & Related papers  (2023-08-29T07:15:57Z)
• PhysDiff: Physics-Guided Human Motion Diffusion Model [101.1823574561535]
  Existing motion diffusion models largely disregard the laws of physics in the diffusion process. PhysDiff incorporates physical constraints into the diffusion process. Our approach achieves state-of-the-art motion quality and improves physical plausibility drastically.
  arXiv  Detail & Related papers  (2022-12-05T18:59:52Z)
• OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion [8.849771760994273]
  We release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo simulator. The model is based on CT scans and dissections used to gather actual muscle data. We also provide a set of reinforcement learning tasks, including reference motion tracking and a reaching task with the neck.
  arXiv  Detail & Related papers  (2021-12-11T19:58:11Z)
• Learning Control Policies for Imitating Human Gaits [2.28438857884398]
  Humans exhibit movements like walking, running, and jumping in the most efficient manner, which served as the source of motivation for this project. Skeletal and Musculoskeletal human models were considered for motions in the sagittal plane. Model-free reinforcement learning algorithms were used to optimize inverse dynamics control actions.
  arXiv  Detail & Related papers  (2021-05-15T16:33:24Z)
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
  We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
  arXiv  Detail & Related papers  (2021-03-26T07:14:01Z)

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.