Bipedal-Walking-Dynamics Model on Granular Terrains
- URL: http://arxiv.org/abs/2604.11981v1
- Date: Mon, 13 Apr 2026 19:12:22 GMT
- Title: Bipedal-Walking-Dynamics Model on Granular Terrains
- Abstract summary: 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.
- Score: 14.386787371962987
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Bipeds have demonstrated high agility and mobility in unstructured environments such as sand. The yielding of such granular media brings significant sinkage and slip of the bipedal feet, leading to uncertainty and instability of walking locomotion. We present a new dynamics-modeling approach to capture and predict bipedal-walking locomotion on granular media. A dynamic foot-terrain interaction model is integrated to compute the ground reaction force (GRF). The proposed granular dynamic model has three additional degree-of-freedom (DoF) to estimate foot sinkage and slip that are critical to capturing robot-walking kinematics and kinetics such as cost of transport (CoT). Using the new model, we analyze bipedal kinetics, CoT, and foot-terrain rolling and intrusion affects. Experiments are conducted using a biped robotic walker on sand to validate the proposed dynamic model with robot-gait profiles, media-intrusion prediction, and GRF estimations. This new dynamics model can further serve as an enabling tool for locomotion control and optimization of bipedal robots to efficiently walk on granular terrains.
Related papers
- Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot [6.048766517808481]
This paper presents an experimentally grounded modeling and optimization framework for a compliant worm robot capable of traversing corrugated pipes.<n>A multi-objective gait optimization problem is formulated to maximize average speed while minimizing average power.<n>Results show that the proposed framework captures the dominant locomotion and energy-consumption behavior of the robot.
arXiv Detail & Related papers (2026-04-13T20:20:03Z) - A Foot Resistive Force Model for Legged Locomotion on Muddy Terrains [13.833218559967348]
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.
arXiv Detail & Related papers (2026-04-13T19:53:52Z) - Inverse Resistive Force Theory (I-RFT): Learning granular properties through robot-terrain physical interactions [7.397755522601659]
We introduce a physics-informed machine learning framework, Inverse Resistive Force Theory (I-RFT), to infer terrain properties.<n>I-RFT preserves physical consistency while enabling generalization across diverse motion primitives.<n>We show that I-RFT accurately estimates terrain properties across multiple gait trajectories and toe shapes.
arXiv Detail & Related papers (2026-03-08T20:30:31Z) - 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) - Contact-Implicit Modeling and Simulation of a Snake Robot on Compliant and Granular Terrain [0.0]
This thesis presents a unified modeling and simulation framework for analyzing sidewinding and tumbling locomotion of the COBRA snake robot.<n>A contact-implicit formulation is used to model distributed frictional interactions during sidewinding.<n>For high-energy rolling locomotion on steep slopes, the Chrono DEM Engine is used to simulate particle-resolved granular interactions.
arXiv Detail & Related papers (2025-12-04T17:20:32Z) - Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
arXiv Detail & Related papers (2025-07-15T17:57:48Z) - Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation [88.83749146867665]
Existing approaches learn a policy to predict a distant next-best end-effector pose.<n>They then compute the corresponding joint rotation angles for motion using inverse kinematics.<n>We propose Kinematics enhanced Spatial-TemporAl gRaph diffuser.
arXiv Detail & Related papers (2025-03-13T17:48:35Z) - 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) - VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait
Representation [78.92147339883137]
We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait.
We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration.
The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
arXiv Detail & Related papers (2022-05-02T19:49:53Z) - 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) - Hybrid Physics and Deep Learning Model for Interpretable Vehicle State
Prediction [75.1213178617367]
We propose a hybrid approach combining deep learning and physical motion models.
We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model.
The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
arXiv Detail & Related papers (2021-03-11T15:21:08Z)
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.