Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration
Authors: Cristina G. Wilson, Marion Nachon, Shipeng Liu, John G. Ruck, J. Diego Caporale, Benjamin E. McKeeby, Yifeng Zhang, Jordan M. Bretzfelder, +15 more
Organizations: Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, 97331, USA · Department of Geology and Geophysics, Texas A&M University, College Station, 77843, USA · Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, 90089, USA · Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, 19104, USA · Department of Astronomy and Planetary Science, Northern Arizona University, Flagstaff, 86011, USA · School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, 30332, USA · NASA Marshall Space Flight Center, Huntsville, 35812, USA · NASA Johnson Space Center, Houston, 77058, USA · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, 19104, USA · Department of Psychology, Temple University, Philadelphia, 19122, USA
The ability to efficiently and effectively explore planetary surfaces is currently limited by the capability of wheeled rovers to traverse challenging terrains, and by pre-programmed data acquisition plans with limited in-situ flexibility. In this paper, we present two novel approaches to address these limitations: (i) high-mobility legged robots that use direct surface interactions to collect rich information about the terrain's mechanics to guide exploration; (ii) human-inspired data acquisition algorithms that enable robots to reason about scientific hypotheses and adapt exploration priorities based on incoming ground-sensing measurements. We successfully verify our approach through lab work and field deployments in two planetary analog environments. The new capability for legged robots to measure soil mechanical properties is shown to enable effective traversal of challenging terrains. When coupled with other geologic properties (e.g., composition, thermal properties, and grain size data etc), soil mechanical measurements reveal key factors governing the formation and development of geologic environments. We then demonstrate how human-inspired algorithms turn terrain-sensing robots into teammates, by supporting more flexible and adaptive data collection decisions with human scientists. Our approach therefore enables exploration of a wider range of planetary environments and new substrate investigation opportunities through integrated human-robot systems that support maximum scientific return.
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Figure 1: The LASSIE approach. Using ( A ) integrated legged locomotion and sensing, ( B ) co-located geoscience measurements across scales, and ( C ) human-robot collaborative algorithms to enhance planetary science exploration.
Figure 2: Analogue field campaigns. Analogues for ( A ) sandy and ( B ) icy environments on the Moon and Mars, where regolith mechanics critically affect robotic operations (insets), were, respectively, ( C ) White Sands National Park, NM and ( D ) Mt. Hood, OR. Representative regolith surface textures investigated at White Sands and Mount Hood are shown in (E–F) and (G). Mars and Moon images IDs: CX00538ML0260438F445256775VA, D001R1239_706529559EDR_F0404_0010M, s_084eff_cyl_sr19c5e_r111m1_0PCT, AS158611599.
Figure 3: White Sands field campaign results.
Figure 4: The human-robot shared autonomy workflow for scientific data collection decisions.
Figure 5: Field measurements at Mt. Hood, OR.
Figure 6: Laboratory robotic intrusion tests into various granular materials and admixtures. ( A ) Robotic intruder leg used in experiments, actuated with two direct-drive gearless motors, which is driven into the adjacent sketch of the experimental air-fluidizing apparatus. ( B ) Conceptual framework relating qualitative rheological trends to the range of observed behaviors encountered in various granular mixtures. ( C ) Force-depth profiles for sand prepared at ϕmin , ϕc , and ϕmax , where each profile is an average of four intrusion tests. Inset: surface displacement profiles. The horizontal dotted line at y = 0 m represents the granular surface. The transparent grey bar symbolizes the intruder location. Negative y values reflect material that has dilated and extended above the surface, while positive y measures correspond to material that has been compacted. Values of x approaching 0 are closer to the center of intrusion. (( D ) Representative force profiles for samples prepared at ϕi = 1, 2, 5, 10, and 15 % .
Figure 7: Mechanically informed, hypothesis-driven surface exploration. ( A ) Legged locomotion enables distributed in situ probing of regolith mechanical properties at every step. ( B ) Proprioceptive force measurements, integrated with complementary payload data (e.g., LiDAR and thermal imaging), detect environmental transitions such as changes in ice content and substrate strength. ( C ) These observations constrain evolving scientific hypotheses and guide adaptive sampling strategies, linking regolith mechanics to landscape formation and volatile-related processes.
Figure S1 : Robotic platforms and scientific instruments deployed at field campaigns.
Deformable granular terrains introduce significant locomotion and immobilization risks in planetary exploration and are difficult to detect via remote sensing (e.g., vision). Legged robots can sense terrain properties through leg-terrain interactions during locomotion, offering a direct means to assess traversability in deformable environments. How to systematically exploit this interaction-derived information for navigation planning, however, remains underexplored. We address this gap by presenting PSANE, a Proprioceptive Safe Active Navigation and Exploration framework that leverages leg-terrain interaction measurements for safe navigation and exploration in unknown deformable environments. PSANE learns a traversability model via Gaussian Process regression to estimate and certify safe regions and identify exploration frontiers online, and integrates these estimates with a reactive controller for real-time navigation. Frontier selection is formulated as a multi-objective optimization that balances safe-set expansion probability and goal-directed cost, with subgoals selected via scalarization over the Pareto-optimal frontier set. PSANE safely explores unknown granular terrain and reaches specified goals using only proprioceptively estimated traversability, while achieving performance improvements over baseline methods.
Matthew Y. Jiang, Feifei Qian, Shipeng Liu
Georgia Institute of Technology, Atlanta, GA 30332, USA · University of Southern California, Los Angeles, CA 90089, USA
Robot-aided exploration of planetary surfaces is essential for understanding geologic processes, yet many scientifically valuable regions, such as Martian dunes and lunar craters, remain hazardous due to loose, deformable regolith. We present a scout-rover cooperation framework that expands safe access to such terrain using a hybrid team of legged and wheeled robots. In our approach, a high-mobility legged robot serves as a mobile scout, using proprioceptive leg-terrain interactions to estimate regolith strength during locomotion and construct spatially resolved terrain maps. These maps are integrated with rover locomotion models to estimate traversal risk and inform path planning. We validate the framework through analogue missions at the NASA Ames Lunar Simulant Testbed and the White Sands Dune Field. Experiments demonstrate (1) online terrain strength mapping from legged locomotion and (2) rover-specific traversal-risk estimation enabling safe navigation to scientific targets. Results show that scout-generated terrain maps reliably capture spatial variability and predict mobility failure modes, allowing risk-aware path planning that avoids hazardous regions. By combining embodied terrain sensing with heterogeneous rover cooperation, this framework enhances operational robustness and expands the reachable science workspace in deformable planetary environments.
Shipeng Liu, J. Diego Caporale, Yifeng Zhang +17
University of Southern California, Los Angeles, CA, USA. · University of Pennsylvania, Philadelphia, PA, USA. · NASA Marshall Space Flight Center, Huntsville, AL, USA. +2
Legged robots are promising candidates for future lunar surface missions because they can traverse steep, loose, and obstacle-rich terrain that challenges conventional wheeled rovers. However, readiness for lunar deployment is limited by uncertainties in foot-regolith interaction, dust generation, illumination-driven perception degradation, and operational constraints. This paper reports lessons from the 2025 LUNA analogue campaign, where ANYmal-D and Magnecko traversed loose regolith simulant and crater-like terrain and collected long-horizon navigation and visual-inertial data under challenging lighting. We show that quadrupedal robots can traverse regolith simulant, but performance is affected by sinkage and slip, dust-generating contacts, and perception failures caused by overexposure, shadows, and low-texture regions. These results motivate tighter integration of regolith-aware locomotion policies, illumination-robust perception, repeatable analogue testing, and mission-level operational validation for future lunar legged robots.
Adrian Fuhrer, Joseph Church, Oliver Fischer +6
ETH Zurich, Robotics Systems Lab; Leonhardstrasse 21, 8092 Zurich, Switzerland