Omnidirectional Amphibious Locomotion via Internal Mass Actuation
cs.RO
Submitted: 2026-09-23
Updated: 2026-09-23
Project page: https://generalroboticslab.com/MARBLE
License: http://creativecommons.org/licenses/by/4.0/
The gist: Field robots must traverse varied terrain and obstacles while remaining robust to water, debris, vegetation, and physical contact.
Terminology
Abstract
Field robots must traverse varied terrain and obstacles while remaining robust to water, debris, vegetation, and physical contact. We present MARBLE, a fully enclosed omnidirectional amphibious rolling robot driven entirely by internal mass redistribution. Three mutually orthogonal linear sliders shift internal masses to generate body rotation, while an orientation-aware controller maps planar velocity commands into slider positions. A rigid spherical shell encloses all active mechanisms and simultaneously serves as the terrestrial contact surface, buoyant enclosure, and mounting structure for passive fins that enable water-surface propulsion. Rotation of the same shell architecture hence produces rolling on land and surface propulsion in water without mechanical reconfiguration or separate locomotion actuators. The spherical morphology further allows the robot to accommodate changes in body orientation and contact location during direct interactions with terrain and obstacles. We evaluate MARBLE through omnidirectional locomotion characterization, traversal across heterogeneous terrestrial environments, aquatic surface locomotion, land-water transitions, and deliberate obstacle interactions. These experiments demonstrate how a single enclosed mechanical architecture can combine omnidirectional mobility, cross-medium locomotion, and tolerance to environmental contact. MARBLE provides a compact design for field mobility across heterogeneous terrain, obstacles, and land-water transitions. We will open-source all software and hardware design. Our website is https://generalroboticslab.com/MARBLE
Sources
- mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
- FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
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