Granular Terrain Locomotion

Latest papers

13 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Oct 17, 2026

Escape Without a Recipe: Maneuver-Agnostic Mars Rover Recovery from Granular Entrapment

Wheeled planetary rovers immobilised in loose regolith have no principled recovery controller: scripted maneuvers are terrain-specific, and naive driving deepens entrapment. We introduce a recurrent policy that learns to escape without a predefined maneuver. The framework rests on a verified trap: in a continuum material point method (MPM) simulator we identify a configuration at sinkage-to-radius ratio z/r = 2.0 where the strongest of ten scripted controllers succeeds in only 7/32 trials, yet the regolith retains traction and continues to shear, confirming granular entrapment rather than a numerical artifact. A frozen entrapment detector, trained on 81,771 windows spanning three materials and six sinkage levels, supplies a three-state belief to the policy observation. A separate simulator mobility latch gates training rewards. Across five training seeds, mean action evaluation achieves 62.5% escape (range 28.1-81.3%; 32 trials per seed) versus 7/32 for the strongest scripted controller and 0/32 for action noise. A numerical audit identifies a coupling-buffer fault that silently discarded 9.7% of contact nodes at 16 parallel environments. Results establish recovery in the tested simulation regime; physical transfer remains unverified.

Meraj Hossain Promit, Chandak Chakma, Md Jubair Ahmed Sourov +1

Department of Robotics & Mechatronics Engineering, University of Dhaka, Bangladesh. · Department of AI & Robotics, New Uzbekistan University, Uzbekistan.

Robot Failure Recovery Robotics Robot Policy Learning Granular Terrain Locomotion

Ask PDF

Oct 2, 2026

Terrain-Dependent Intra-Cycle Leg Timing for Effective Locomotion on Granular Slopes

Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media. Using a hexapedal robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced performance loss is primarily governed by delayed anchoring and increased backward slip rather than excessive sinkage. By extending the model to generalized terrain conditions, we construct failure phase diagrams that identify sinkage- and slippage-induced failure regimes, enabling quantitative risk estimation for locomotion on granular slopes. This physics-informed framework provides predictive insight into terrain-dependent failure mechanisms and offers guidance for safer and more robust robot operation on deformable inclines.

Long Zhao, Xingjue Liao, Feifei Qian

Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.

Granular Terrain Locomotion Robot Locomotion Terrain-Aware Robot Locomotion Legged Robotics Gait Analysis

Ask PDF

Sep 9, 2026

GM-Loco: Terrain-Adaptive Humanoid Locomotion on Granular Media

Humanoid locomotion on granular terrain remains a significant challenge due to its complex foot-terrain interaction dynamics that are difficult to model. Existing approaches either ignore granular contact dynamics or incorporate simplified normal force models with heuristic tangential components. In this work, we present a physics-grounded granular contact model based on three-dimensional resistive force theory (3D RFT) and efficiently simulate granular terrain for reinforcement learning (RL) training. Unlike traditional rigid contact models and simplified granular contact models with ad-hoc heuristics, our contact solver produces physically accurate granular intrusion dynamics without resorting to heuristics. It captures realistic penetration and tangential drag during training, enabling the policy to learn behaviors that transfer reliably to real-world granular terrain where rigid contact models fail. To adapt to varying terrain conditions, we train a terrain-adaptive locomotion controller via teacher-student RL, using a variational autoencoder to encode terrain information into a compact latent representation. Simulation studies using material point method (MPM) with NVIDIA Newton demonstrate that our method generalizes to unseen granular terrains, achieves a significantly higher success rate than baselines, and demonstrates zero-shot terrain identification and adaptation. We further validate our approach through extensive hardware experiments across diverse real-world granular terrains including basalt, dry sand, and beach sand. To the best of our knowledge, this is the first demonstration of agile humanoid locomotion on real-world granular terrain. Project page: https://humanoid-gm-locomotion.github.io/HUMANOID-GM/

Junnosuke Kamohara, Feiyang Wu, Andy Ningan Zong +4

Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA · School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA · Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA

Humanoid Robot Locomotion Granular Terrain Locomotion Terrain-Aware Robot Locomotion Robot Locomotion RL for Legged Locomotion

Ask PDF

Jul 13, 2026

Robust bipedal locomotion on flowable slopes via foot-driven terrain manipulation

Bipedal robots are challenging to control because they operate close to instability, where small variations in foot-terrain contact can rapidly destabilize locomotion. On rigid terrain, bipedal robots mitigate this fragility by using well-established contact mechanics and control strategies. On flowable surfaces such as granular slopes, foot contact can induce large surface deformations and solid-fluid-like transitions, coupling terrain effects with robot dynamics, leading to underperformance or failure. This is partly due to the lack of reliable methods to represent the dynamics of flowable terrain, making it difficult to account for terrain effects in locomotion design. Here, we investigate how controlling terrain response can improve bipedal locomotion on granular slopes by studying the terradynamics of cleated feet, thin plates emanating from the foot soles. Systematic studies of a small-scale (1.4 kg) robophysical biped reveal that cleats with sparse and dense spacing lead to excessive terrain yielding and resistance, respectively, degrading performance and leading to failure. An intermediate cleat spacing distributes interaction forces to maintain substrate stresses near (or below) the yield threshold, enabling walking on granular slopes up to 30 degrees. Guided by these principles, we design a foot that actively adjusts cleat depth and accommodates both rigid and granular terrain. We also demonstrate that the principles of effective foot-terrain interaction translate to a larger (15 kg) autonomous biped. Our study presents an alternative to conventional body-centric robot control approaches, which regulate terrain-induced effects through body motion, by instead regulating terrain interactions through limb-centric approach.

Deniz Kerimoglu, Junnosuke Kamohara, Jiyeon Maeng +4

Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA · School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA · Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA

Humanoid Robot Locomotion Granular Terrain Locomotion Terrain-Aware Robot Locomotion Robot Locomotion

Ask PDF

Jun 17, 2026

Simulating Robotic Locomotion in Sand: Resistive Force Theory in an Open-Source Physics Engine

Recent advancements in Resistive Force Theory (RFT) enable approximation of ground reaction forces for locomotion in sand without the computational expense of modeling interactions with individual grains. However, these tools have been absent in 3D physics engines commonly used for robot simulation. We explore if resistive force approximations are sufficient, when integrated with standard dynamics calculations, to provide a stable substrate for a freely walking robot. To determine this, we implement 3D Granular Resistive Force Theory (3D RFT) in a physics simulation engine, MuJoCo. We verify simulations in multiple scenarios to demonstrate that key trends due to end effector shape, speed, and loading are preserved. Our implementation predicts both walking distance and foot sinkage of a 12-Degree of Freedom hexapod robot within 7% of experiments in sand. While RFT has inherent approximations, the open source tool described here has potential to help develop new and improved robot designs to traverse granular media substrates.

Ryan Walker Brown, Laura K. Treers, Kathryn A. Daltorio

College of Engineering, Department of Mechanical and Aerospace Engineering at Case Western Reserve University, Cleveland, OH 44106 · College of Engineering and Mathematical Sciences, Department of Mechanical Engineering at the University of Vermont, Burlington, VT 05405

Granular Terrain Locomotion Robot Locomotion Physics-Based Simulation

Ask PDF

Jun 9, 2026

Locomotion analysis of a quadruped interacting with the lunar granular surface

Deploying legged robots in extra-terrestrial environments includes many challenges due to complex terrain interactions, energy, and thermal constraints. For effective mechanical design of a lunar exploration quadrupedal robot, careful consideration of motor torques, energy expenditure, and cost of transport is required. The lunar surface is composed of granular regolith, which impacts the locomotion of legged robots and their performance. Locomotion algorithms trained with rigid contact assumptions are also ineffective when applied to environments with soft contacts, such as granular surfaces, which can result in instability and poor tracking. In this report, the physical modelling of the granular lunar surface-robot foot contacts is applied to a simulation environment with locomotion trained using Reinforcement Learning. A comparison is conducted between the policy trained on rigid contact and soft contact environments, analysing the gait and locomotion performance metrics. The analysis demonstrates that soft contacts simulating regolith surfaces pose additional challenges for Reinforcement Learning based training, result in a qualitatively different gait, and increase the overall energy expenditure.

Yash J Vyas

Department of Industrial Engineering, University of Padua

Granular Terrain Locomotion Robotics Simulation Gait Analysis Robotics Terrain-Aware Robot Locomotion Robot Locomotion+2

Ask PDF

Jun 5, 2026

Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension

This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution. A single neural network controller, trained to track a desired path across challenging terrain, fully unlocks the capabilities of this actuated suspension system for autonomous obstacle negotiation. A reinforcement learning framework is developed using the high-fidelity DARTS simulation engine, which combines rigid-contact dynamics and Bekker-Wong terramechanics, enabling the emergence of locomotion strategies adapted to loose-soil conditions. To obtain a single unified controller across heterogeneous terrains, a policy consolidation strategy merges the experience of terrain-specialized agents into one neural network, eliminating the need for explicit terrain classification and controller switching. The resulting controller operates on a combination of proprioceptive and exteroceptive feedback, including sparse stereo-derived terrain elevation, chassis attitude, joint states, and force-torque measurements. Zero-shot transfer to the physical rover is achieved through domain randomization, sensor noise injection, and model-to-real system identification. Experimental results demonstrate autonomous traversal of rock fields, a Bickler trap (bump obstacle), a wheel-high step, sand ripples, and sandy slopes. On a 20° sandy slope, the learned controller reduces the cost of transport by 37% on dry sand despite the additional actuation, and achieves superior performance on wet sand where the passive suspension becomes completely immobilized. A video accompanying this paper is available at https://youtu.be/d684P5a3xMc

Arthur Bouton, Tristan D. Hasseler, Michael Paton +5

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA · Center for Autonomy, University of Texas at Austin, Austin, TX 78712 USA · Space Systems Laboratory, University of Maryland, MD 20742 USA

Feedback Control Off-Road Robot Navigation Robotic RL Robotics Terrain-Aware Robot Locomotion Vehicle Dynamics+2

Ask PDF

May 23, 2026

Terrain-Adaptive Grouser Wheel for Optimal Planetary Exploration: Design and Experimental Investigation

Planetary rovers operating in extraterrestrial environments often encounter significant mobility challenges due to varying terrain features such as gradients and granularity. While recent works in multimodal wheel design have explored adjustments in stiffness, compliance, and diameter as a means to improve terrain adaptability, full wheel grouser-adjustable designs remain largely unexplored. Grousers are a compelling feature to actuate, as granular terrains tend to require increased grouser height for improved wheel performance. As a result, we introduce [Anonymized Robot Name], a multimodal wheel capable of continuously adjusting its grouser height for terrain adaptation. The platform was evaluated across four representative surfaces, including vinyl flooring, coarse rock, pea gravel, and sand under two packing states, spanning a range of granular conditions. Results from 750 experimental trials demonstrate that adaptive deployment reduces slip by 30.0--58.0% and improves travel time and energy consumption by up to 77.4% in granular regimes relative to fixed configurations. Using the terrain trial data, a simplified scaling analysis was developed and validated, suggesting a relationship between terrain granularity and optimal grouser height for the tested configuration. No single grouser height minimized slip across all terrains, underscoring the limitations of fixed-wheel systems commonly used for planetary exploration. This observation reinforces the potential of grouser-adaptive morphology, such as [Anonymized Robot Name], as an effective solution for enhancing rover mobility across diverse and mobility-challenging extraterrestrial environments.

Vincent Griffo, Yashwanth Kumar Nakka

Aerospace Robotics Lab, Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Granular Terrain Locomotion Robotics Robot Locomotion

Ask PDF

Apr 13, 2026

Bipedal-Walking-Dynamics Model on Granular Terrains

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.

Xunjie Chen, Xinyan Huang, Peter Shan +2

Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854 USA · Bridgewater Raritan High School, Bridgewater, NJ 08807 USA · State Key Lab of Fluid Power Transmission and Control and the School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

Granular Terrain Locomotion Terrain-Aware Robot Locomotion

Ask PDF

Apr 2, 2026

From Impact to Insight: Dynamics-Aware Proprioceptive Terrain Sensing on Granular Media

Robots that traverse natural terrain must interpret contact forces generated under highly dynamic conditions. However, most terrain characterization approaches rely on quasi-static assumptions that neglect velocity- and acceleration-dependent effects arising during impact and rapid stance transitions. In this work, we investigate granular terrain interaction during high-speed hopping and develop a physics-based framework for dynamic terrain characterization using proprioceptive sensing alone. Through controlled hopping experiments with systematically varied impact speed and leg compliance, our measurements reveal that quasi-static based assumptions lead to large discrepancies in granular terrain property estimation during high-speed hopping, particularly upon touchdown and controller-induced stiffness transitions. Velocity-dependent drag alone cannot explain these discrepancies. Instead, acceleration-dependent added-mass effects - associated with grain entrainment beneath the foot - dominate transient force responses. We integrate this force decomposition with a momentum-observer-based estimator that compensates for rigid-body inertia and gravity, and introduce an acceleration-aware weighted regression to account for increased force variance during high-acceleration events. Together, these methods enable consistent recovery of granular resistance across locomotion conditions, closely matching linear-actuator ground truth. Our results demonstrate that accurate terrain inference during high-speed locomotion requires explicit treatment of acceleration-dependent granular effects, and provide a foundation for robots to characterize complex deformable terrain during dynamic exploration of terrestrial and planetary environments.

Yifeng Zhang, Yue Wu, Jake Futterman +5

Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA

Terrain-Aware Robot Locomotion Granular Terrain Locomotion

Ask PDF

Mar 8, 2026

Inverse Resistive Force Theory (I-RFT): Learning granular properties through robot-terrain physical interactions

For robots to navigate safely and efficiently on soft, granular terrains, it is crucial to gather information about the terrain's mechanical properties, which directly affect locomotion performance. Recent research has developed robotic legs that can accurately sense ground reaction forces during locomotion. However, existing tests of granular property estimation often rely on specific foot trajectories, such as vertical penetration or horizontal shear, limiting their applicability during natural locomotion. To address this limitation, we introduce a physics-informed machine learning framework, Inverse Resistive Force Theory (I-RFT), which integrates the Granular Resistive Force Theory model with Gaussian Processes to infer terrain properties from proprioceptively measured contact forces under arbitrary gait trajectories. By embedding the granular force model within the learning process, I-RFT preserves physical consistency while enabling generalization across diverse motion primitives. Experimental results demonstrate that I-RFT accurately estimates terrain properties across multiple gait trajectories and toe shapes. Moreover, we show that the quantified uncertainty over the terrain resistance stress map could enable robots to optimize foot design and gait trajectories for efficient information gathering. This approach establishes a new foundation for data-efficient characterization of complex granular environments and opens new avenues for locomotion strategies that actively adapt gait for autonomous terrain exploration.

Shipeng Liu, Feng Xue, Yifeng Zhang +2

University of Southern California, Los Angeles, CA 90089, USA

Terrain-Aware Robot Locomotion Granular Terrain Locomotion Data-Efficient Robot Learning Physics-Informed ML

Ask PDF

Mar 6, 2026

Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes

Xingjue Liao, Feifei Qian

Granular Terrain Locomotion Robot Locomotion Terrain-Aware Robot Locomotion

Ask PDF

Mar 6, 2026

Terrain characterization and locomotion adaptation in a small-scale lizard-inspired robot

Unlike their large-scale counterparts, small-scale robots are largely confined to laboratory environments and are rarely deployed in real-world settings. As robot size decreases, robot-terrain interactions fundamentally change; however, there remains a lack of systematic understanding of what sensory information small-scale robots should acquire and how they should respond when traversing complex natural terrains. To address these challenges, we develop a Small-scale, Intelligent, Lizard-inspired, Adaptive Robot (SILA Bot) capable of adapting to diverse substrates. We use granular media of varying depths as a controlled yet representative terrain paradigm. We show that the optimal body movement pattern (ranging from standing-wave bending that assists limb retraction on flat ground to traveling-wave undulation that generates thrust in deep granular media) can be parameterized and approximated as a linear function of granular depth. Furthermore, proprioceptive signals, such as joint torque, provide sufficient information to estimate granular depth via a K-Nearest Neighbors classifier, achieving 95% accuracy. Leveraging these relationships, we design a simple linear feedback controller that modulates body phase and substantially improves locomotion performance on terrains with unknown depth. Together, these results establish a principled framework for perception and control in small-scale locomotion and enable effective terrain-adaptive locomotion while maintaining low computational complexity.

Duncan Andrews, Landon Zimmerman, Evan Martin +2

Locomotion in Biology and Robotics (LiBR) Lab at Penn State University, University Park, PA 16802

Bioinspired Robotics Robotics Terrain-Aware Robot Locomotion Robot Locomotion Robotic Control Granular Terrain Locomotion

Ask PDF