Daniel E. Koditschek

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.

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.

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.

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.

Scientists and engineers alike target modeling of complex, high dimensional, and nonlinear dynamical systems as a central goal. Machine learning breakthroughs alongside mounting computation and data advance the efficacy of learning from trajectory measurements. However scientifically interpreting data-driven models, e.g., localizing attracting sets and their basins, remains elusive. Such limitations particularly afflict identification of system-level regulatory mechanisms characteristic of living systems, e.g., stabilizing control for whole-body locomotion, where discontinuous, transient, and multiscale phenomena are common and prior models are rare. As a next step towards theory-grounded discovery of behavioral mechanisms in biology and beyond, we introduce VERT, a framework for discovering attracting sets from trajectories without recourse to any global model. Our infinitesimal-local-global (ILG) pipeline estimates the proximity of any sampled state to an attracting set, if one exists, with formal accuracy guarantees. We demonstrate our approach on phenomenological and physical oscillators with hierarchical and impulsive dynamics, finding sensitivity to both global and intermediate attractors composed in sequence and parallel. Application of VERT to human running kinematics data reveals insight into control modules that stabilize task-level dynamics, supporting a longstanding neuromechanical control hypothesis. The VERT framework promotes rigorous inference of underlying dynamical structure even for systems where learning a global dynamics model is impractical or impossible.

Abstract Moving down a hillslope from ridge to valley, soil develops and becomes increasingly weathered. Downslope variation in clay content, organic matter, and porosity should produce concomitant changes in soil strength that influence slope stability and erosion. This has yet to be demonstrated, however, because in situ measurements of soil rheology are challenging and rare. Here we employ a robotic leg as a mechanically sensitive and time‐efficient penetrometer to map soil strength along a canonical temperate hillslope profile. We observe a systematic downslope weakening, and increasing heterogeneity, of soil strength associated with a transition from sand‐rich ridge materials to cohesive valley bottom soil aggregates. Weathering‐induced changes in soil composition lead to physically distinct mechanical behaviors in cohesive soils that depart from the behavior observed for sand. We also demonstrate the promise that legged robots may use their limbs to sense and improve mobility in complex environments, with implications for planetary exploration.

Moving down a hillslope from ridge to valley, soil develops and becomes increasingly weathered. Downslope variation in clay content, organic matter, and porosity should produce concomitant changes in soil strength that influence slope stability and erosion. This has yet to be demonstrated, however, because in-situ measurements of soil rheology are challenging and rare. Here we employ a robotic leg as a mechanically sensitive and time-efficient penetrometer to map soil strength along a canonical temperate hillslope profile. We observe a systematic downslope weakening, and increasing heterogeneity, of soil strength associated with a transition from sand-rich ridge materials to cohesive valley bottom soil aggregates. Weathering-induced changes in soil composition lead to physically distinct mechanical behaviors in cohesive soils that depart from the behavior observed for sand. We also demonstrate the promise that legged robots may use their limbs to sense and improve mobility in complex environments, with implications for planetary exploration.

This paper develops a three-degree-of-freedom sagittal-plane hybrid dynamical systems model of a Groucho-style bounding quadrupedal run. Simple within-stance controls using a modular architecture yield a closed-form expression for a family of hybrid limit cycles that represent bounding behavior over a range of user-selected fore-aft speeds as a function of the model’s kinematic and dynamical parameters. Controls acting on the hybrid transitions are structured so as to achieve a cascade composition of in-place bounding driving the fore-aft degree of freedom, thereby decoupling the linearized dynamics of an approximation to the stride map. Careful selection of the feedback channels used to implement these controls affords infinitesimal deadbeat stability, which is relatively robust against parameter mismatch. Experiments with a physical quadruped reasonably closely match the bounding behavior predicted by the hybrid limit cycle and its stable linearized approximation.

Modern legged robot morphologies assign most of their actuated degrees of freedom (DoF's) to the limbs and designs continue to converge to twelve DoF quadrupeds with three actuators per leg and a rigid torso often modeled as a Single Rigid Body (SRB). This is in contrast to the animal kingdom, which provides tantalizing hints that core actuation of a jointed torso confers substantial benefit for efficient agility. Unfortunately, the limited specific power of available actuators continues to hamper roboticists' efforts to capitalize on this bio-inspiration. This paper presents the initial steps in a comparative study of the costs and benefits associated with a traditionally neglected torso degree of freedom: a twisting spine. We use trajectory optimization to explore how a one-DoF, axially twisting spine might help or hinder a set of axially-active (twisting) behaviors: trots, sudden turns while bounding, and parkour-style wall jumps. By optimizing for minimum electrical energy or average power, intuitive cost functions for robots, we avoid hand-tuning the behaviors and explore the activation of the spine. Initial evidence suggests that for lower energy behaviors the spine increases the electrical energy required when compared to the rigid torso, but for higher energy runs the spine trends toward having no effect or reducing the electrical work. These results support future, more bio-inspired versions of the spine with inherent stiffness or dampening built into their mechanical design.

This paper introduces a new controller that stabilizes the motion of a spatial quadruped around sagittal-plane templates. It enables highly dynamic gaits and transitional maneuvers formed from parallel and sequential compositions of such planar templates in settings that require significant out-of-plane reactivity. The controller admits formal guarantees of stability with some modest assumptions. Experimental results validate the reliable execution of those planar template-based maneuvers, even in the face of large lateral, yaw, and roll incurring disturbances. This spatial anchor, fixed in parallel composition with a variety of different parallel and sequential compositions of sagittal plane templates, illustrates the robust portability of provably interoperable modular control components across a variety of hardware platforms and behaviors.

A vast number of applications for legged robots entail tasks in complex, dynamic environments. But these environments put legged robots at high risk for limb damage. This paper presents an empirical study of fault tolerant dynamic gaits designed for a quadrupedal robot suffering from a single, known "missing" limb. Preliminary data suggests that the featured gait controller successfully anchors a previously developed planar monopedal hopping template in the three-legged spatial machine. This compositional approach offers a useful and generalizable guide to the development of a wider range of tripedal recovery gaits for damaged quadrupedal machines.