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

• URL: http://arxiv.org/abs/2606.06790v1
• Date: Fri, 05 Jun 2026 00:20:50 GMT
• Title: Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
• Abstract summary: ERNEST is a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension.<n>A single neural network controller is trained to track a desired path across challenging terrain.
• Score: 0.9612340765846284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 bump trap, 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.

Related papers

• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning [6.13534373131836]
  This paper presents ASTER, a robust RL framework that achieves, to our knowledge, the first successful autonomous inverted flight for the cable-suspended system.<n>We show remarkable agility, precise attitude alignment, and robust zero-shot sim-to-real transfer across complex trajectories.
  arXiv  Detail & Related papers  (2026-03-11T12:40:29Z)
• Dynamic Modeling and Attitude Control of a Reaction-Wheel-Based Low-Gravity Bipedal Hopper [0.0]
  hopping locomotion offers high energy efficiency but is prone to mid-flight attitude instability.<n>This paper presents an underactuated bipedal hopping robot that employs an internal reaction wheel to regulate body posture.<n>The proposed framework is evaluated in MuJoCo-based simulations under lunar gravity conditions.
  arXiv  Detail & Related papers  (2026-03-11T11:35:21Z)
• HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control [67.58927286226925]
  We propose a learning-based framework that integrates humanoid-skateboard system modeling and physics-aware whole-body control.<n>Experiments on the Unitree G1 humanoid platform demonstrate that our framework enables stable and agile maneuvering on skateboards in real-world scenarios.
  arXiv  Detail & Related papers  (2026-02-03T07:18:01Z)
• Towards reliable subsea object recovery: a simulation study of an auv with a suction-actuated end effector [1.1984905847118061]
  This paper presents a simulation-based study of a complete autonomous subsea object recovery mission using a Hadal Small Vehicle (HSV)<n>The Stonefish simulator is used to model realistic vehicle dynamics, hydrodynamic disturbances, sensing, and interaction with a target object under hadal-like conditions.<n>The HSV autonomously descends from the sea surface to 6,000 m, performs structured seafloor coverage, detects a target object, and executes a suction-based recovery.
  arXiv  Detail & Related papers  (2026-01-03T07:55:23Z)
• Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction [86.53248703859718]
  We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy.<n>Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings.
  arXiv  Detail & Related papers  (2025-12-08T11:42:41Z)
• Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning [0.6456676618238324]
  This paper deals with an active suspension system focused on chassis stabilisation. SAC was applied along with Proportional Integral Derivative (PID) control to stabilise the chassis. The model uses the rover's distance from surrounding obstacles, the height of the obstacle, and the chassis' orientation to actuate the control links of the suspension accurately.
  arXiv  Detail & Related papers  (2024-06-27T05:27:39Z)
• Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots [50.02055068660255]
  Navigating urban environments poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain.
  arXiv  Detail & Related papers  (2024-05-03T00:29:20Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• 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)
• Control of rough terrain vehicles using deep reinforcement learning [0.0]
  This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle. The carefully shaped reward signal promotes safe, environmental, and efficient driving. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites.
  arXiv  Detail & Related papers  (2021-07-05T08:43:05Z)

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.