Chen Li

Planning is the process of evaluating alternative futures to predict outcomes and guide action. Evidence for this process exists in several vertebrates, but remains contested in in-vertebrates. Jumping spiders in the genus Portia show planning-like behavior in lab studies, but how this translates to the wild is unclear, where hunts of potentially lethal spider prey occur over durations of more than an hour in dense foliage. We present the first fully video-recorded wild hunt of any Portia species, with a 3D reconstruction of its forty-two minute long route to attack an orb-weaving spider. The reconstruction suggests P. labiata may have planned its approach, avoiding a route half as long but potentially more conspicuous to the prey's sensory systems.

Animals and robots encounter mud at the water–land interface. Like sand, mud can stay solid or flow like a fluid. Unlike sand, the yield strength of mud at which solid–fluid transitions occur depends on not only the amount of solid relative to fluid (water in mud, air in dry sand), but also how much coarse grains and fine clay are within the solid. Despite understanding of locomotion on/within dry sand dominated by coarse grains with repulsive normal forces and friction, little is known for mud dominated by fine clay with strong cohesion. Here, we developed methods to prepare uniform mud of controlled, variable yield strength and characterize and track its drift from water evaporation. Compared to other flowable substrates, mud strength measured by upward force during penetration is weaker and can vary more, and mud sticks more during extraction to pull downward, making it more challenging for locomotion.

Synopsis: Animals and robots must self-right on the ground after overturning. Biology research has described various strategies and motor patterns in many species. Robotics research has devised many strategies. However, we do not well understand the physical principles of how the need to generate mechanical energy to overcome the potential energy barrier governs behavioral strategies and 3D body rotations given the morphology. Here, I review progress on this which I led studying cockroaches self-righting on level, flat, solid, low-friction ground, by integrating biology experiments, robotic modeling, and physics modeling. Animal experiments using three species (Madagascar hissing, American, and discoid cockroaches) found that ground self-righting is strenuous and often requires multiple attempts to succeed. Two species (American and discoid cockroaches) often self-right dynamically, using kinetic energy to overcome the barrier. All three species use and often stochastically transition across diverse strategies. In these strategies, propelling motions are often accompanied by perturbing motions. All three species often display complex yet stereotyped body rotation. They all roll more in successful attempts than in failed ones, which lowers the barrier, as revealed by a simplistic 3D potential energy landscape of a rigid body self-righting. Experiments of an initial robot self-righting via rotation about a fixed axis revealed that the longer and faster appendages push, the more mechanical energy can be gained to overcome the barrier. However, the cockroaches rarely achieve this. To further understand the physical principles of strenuous ground self-righting, we focused on the discoid cockroach’s leg-assisted winged self-righting. In this strategy, wings propel against the ground to pitch the body up but are unable to overcome the highest pitch barrier. Meanwhile, legs flail in the air to perturb the body sideways to self-right via rolling. Experiments using a refined robot and an evolving 3D potential energy landscape revealed that, although wing propelling cannot generate sufficient kinetic energy to overcome the highest pitch barrier, it reduces the barrier to allow small kinetic energy from the perturbing legs to probabilistically overcome the barrier to self-right via rolling. Thus, only by combining propelling and perturbing can self-righting be achieved when it is so strenuous; this physical constraint leads to the stereotyped body rotation. Finally, multi-body dynamics simulation and template modeling revealed that the animal’s substantial randomness in wing and leg motions helps it, by chance, to find good coordination, which accumulates more mechanical energy to overcome the barrier, thus increasing the likelihood of self-righting.

Robots can traverse sparse obstacles by sensing environmental geometry and avoiding contact with obstacles. However, for search and rescue in rubble, environmental monitoring through dense vegetation, and planetary exploration over Martian and lunar rocks, robots must traverse cluttered obstacles as large as themselves by physically interacting with them. Previous work discovered that the forest floor-dwelling discoid cockroach and a sensor-less minimalistic robot can traverse cluttered grass-like beam obstacles of various stiffness by transitioning across different locomotor modes. Yet the animal was better at traversal than the sensor-less robot, likely by sensing forces during obstacle interaction to control its locomotor transitions. Inspired by this, here we demonstrated in simulation that environmental force sensing helps robots traverse cluttered large obstacles. First, we developed a multi-body dynamics simulation and a physics model of the minimalistic robot interacting with beams to estimate beam stiffness from the sensed contact forces. Then, we developed a force feedback strategy for the robot to use the sensed beam stiffness to choose the locomotor mode with a lower mechanical energy cost. With feedforward pushing, the robot was stuck in front of stiff beams if it has a limited force capacity; without force limit, it traversed but suffered a high energy cost. Using obstacle avoidance, the robot traversed beams by avoiding beam contact regardless of beam stiffness, resulting in a high energy cost for flimsy beams. With force feedback, the robot determined beam stiffness, then traversed flimsy beams by pushing them over and stiff beams by rolling through the gap between them with a low energy cost. Stiffness estimation based on force sensing was accurate across varied body oscillation amplitude and frequency and position sensing uncertainty. Mechanical energy cost of traversal increased with sensorimotor delay. Future work should demonstrate cluttered large obstacle traversal using force feedback in a physical robot.

Snakes can bend their elongate bodies in various forms to traverse various environments. We understand well how snakes use lateral body bending to push against asperities on flat ground for propulsion, and snake robots can do so effectively. However, snakes can also use vertical bending to push against uneven terrain of large height variation for propulsion, and they can adjust this bending to adapt to novel terrain presumably using mechano-sensing feedback control. Although some snake robots can traverse uneven terrain, few have used vertical bending for propulsion, and how to control this process in novel environments is poorly understood. Here we systematically studied a snake robot with force sensors pushing against large bumps using vertical bending to understand the role of sensory feedback control. We compared a feedforward controller and four feedback controllers that use different sensory information and generate distinct bending patterns and body-terrain interaction. We challenged the robot with increasing backward load and novel terrain geometry that break its contact with the terrain. We further varied how much the feedback control modulated body bending to conform to or push against the terrain to test their effects. Feedforward propagation of vertical bending generated large propulsion when the bending shape matched terrain geometry. However, when perturbations caused loss of contact, the robot easily lost propulsion or had motor overload. Contact feedback control resolved these issues by helping the robot regain contact. Yet excessive conformation interrupted shape propagation and excessive pushing stalled motors frequently. Unlike that using lateral bending, for propulsion generation using vertical bending, body weight that can help maintain contact with the environment but may also overload motors. Our results will help snake robots better traverse uneven terrain with large height variation and can inform how snakes use sensory feedback to control vertical body bending for propulsion.

Outside laboratory conditions and human-made structures, animals rarely encounter flat surfaces. Instead, natural substrates are uneven surfaces with height variation that ranges from the microscopic scale to the macroscopic scale. For walking animals (which we define as encompassing any form of legged movement across the ground, such as walking, running, galloping, etc.), such substrate ‘roughness’ influences locomotion in a multitude of ways across scales, from roughness that influences how each toe or foot contacts the ground, to larger obstacles that animals must move over or navigate around. Historically, the unpredictability and variability of natural environments has limited the ability to collect data on animal walking biomechanics. However, recent technical advances, such as more sensitive and portable cameras, biologgers, laboratory tools to fabricate rough terrain, as well as the ability to efficiently store and analyze large variable datasets, have expanded the opportunity to study how animals move under naturalistic conditions. As more researchers endeavor to assess walking over rough terrain, we lack a consistent approach to quantifying roughness and contextualizing these findings. This Review summarizes existing literature that examines non-human animals walking on rough terrain and presents a metric for characterizing the relative substrate roughness compared with animal size. This framework can be applied across terrain and body scales, facilitating direct comparisons of walking over rough surfaces in animals ranging in size from ants to elephants.

Many applications require robots to move through complex 3-D terrain with large obstacles, such as self-driving, search and rescue, and extraterrestrial exploration. Although robots are already excellent at avoiding sparse obstacles, they still struggle in traversing cluttered large obstacles. To make progress, we need to better understand how to use and control the physical interaction with obstacles to traverse them. Forest floor-dwelling cockroaches can use physical interaction to transition between different locomotor modes to traverse flexible, grass-like beams of a large range of stiffness. Inspired by this, here we studied whether and how environmental force sensing helps robots make active adjustments to traverse cluttered large obstacles. We developed a physics model and a simulation of a minimalistic robot capable of sensing environmental forces during traversal of beam obstacles. Then, we developed a force-feedback control strategy, which estimated beam stiffness from the sensed contact force using the physics model. Then in simulation we used the estimated stiffness to control the robot to either stay in or transition to the more favorable locomotor modes to traverse. When beams were stiff, force sensing induced the robot to transition from a more costly pitch mode to a less costly roll mode, which helped the robot traverse with a higher success rate and less energy consumed. By contrast, if the robot simply pushed forward or always avoided obstacles, it would consume more energy, become stuck in front of beams, or even flip over. When the beams were flimsy, force sensing guided the robot to simply push across the beams. In addition, we demonstrated the robustness of beam stiffness estimation against body oscillations, randomness in oscillation, and uncertainty in position sensing. We also found that a shorter sensorimotor delay reduced energy cost of traversal.

A challenge to understanding locomotion in complex three-dimensional terrain with large obstacles is to create tools for controlled, systematic experiments. Recent terrain arenas allow observations at small spatiotemporal scales (∼10 body lengths or cycles). Here, we created a terrain treadmill to enable high-resolution observation of animal locomotion through large obstacles over large spatiotemporal scales. An animal moves through modular obstacles on an inner sphere, while a rigidly attached, concentric, transparent outer sphere rotates with the opposite velocity via closed-loop feedback to keep the animal on top. During sustained locomotion, a discoid cockroach moved through pillar obstacles for up to 25 min (2500 cycles) over 67 m (1500 body lengths). Over 12 trials totaling ∼1 h, the animal was maintained within a radius of 1 body length (4.5 cm) on top of the sphere 90% of the time. The high-resolution observation enables the study of diverse locomotor behaviors and quantification of animal–obstacle interaction.