Justin Werfel

Natural landscape morphology is the emergent result of continuous, reciprocal interactions between biological agents and their physical environment. While agent-based modeling has been successfully utilized to simulate human-environment dynamics in urban settings, its application to understanding non-human geomorphological change remains an underexplored frontier. This paper presents a bio-inspired multi-agent system framework to investigate how individual animal behaviors — specifically those of the North American beaver — shape the development of adaptive landscapes. We introduce a specialized agent-based architecture in which key beaver behaviors are modeled by two different agent types with distinct ecological roles: Explorers, which drive spatial diffusion and resource identification, and Builders, which reinforce environmental traces through localized engineering. Utilizing a dynamic environment characterized by seasonal vegetation cycles and coupled hydrology, we demonstrate that simple behavioral heuristics can trigger significant geomorphological shifts. Our results show that while smaller colonies maintain ecological homeostasis, larger ones cross a threshold that activates hydrological expansion and riverbed widening. This work provides an open-source tool and methodology for simulating regenerative strategies harnessing natural processes and non-human agency, for use in landscape architecture and environmental design.

In crowded environments, individuals must navigate around other occupants to reach their destinations. Understanding and controlling traffic flows in these spaces is relevant for coordinating robot swarms and designing infrastructure for dense populations. Here, we use simulations, theory, and experiments to study how adding stochasticity to agent motion can reduce traffic jams and help agents travel more quickly to prescribed goals. A computational approach reveals the collective behavior. Above a critical noise level, large jams do not persist. From this observation, we analytically approximate the swarm's goal attainment rate, which allows us to solve for the agent density and noise level that maximize the goals reached. Robotic experiments corroborate the behaviors observed in our simulated and theoretical results. Finally, we compare simple, local navigation approaches with a sophisticated but computationally costly central planner. A simple reactive scheme performs well up to moderate densities and is far more computationally efficient than a planner, motivating further research into robust, decentralized navigation methods for crowded environments. By integrating ideas from physics and engineering using simulations, theory, and experiments, our work identifies new directions for emergent traffic research.

California blackworms constitute a recently identified animal system exhibiting unusual collective behaviors, in which dozens to thousands of worms entangle to form a “blob” capable of actions like locomotion as an aggregate. In this paper we describe a system of pneumatic soft robots inspired by the blackworms, intended for the study of collective behaviors enabled and mediated by such physical entanglement. Both the robots and worms have high aspect ratio (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gtrsim 1: 50$</tex>), intertwine in complex 3D configurations, operate both in air and underwater, and can locomote both individually and as a collective. We demonstrate and characterize locomotion for both individual robots and entangled blobs, explore the tunability of entanglement strength, and compare these to the analogous versions in living worms. The robots provide a testbed for studying mechanisms underlying behaviors observed in worm blobs, as well as serving as a platform for studies of novel collective behaviors based on physical entanglement.

“Robot factors” design, analogous to ergonomics for humans, seeks to create devices and equipment that can be readily operated by robots, by considering typical capabilities of current robots throughout the design process. While a number of principles and heuristics for robot factors design have been identified, the successful design of hardware operable by autonomous robots often depends in practice on the designer's intuition about robot capabilities, developed through personal experience working with robots. Here we present a tool we have developed to help evaluate a potential device design for usability by a robot, by allowing a designer to in effect teleoperate a virtual robot and attempt the operation of the device. The tool uses a 3D physics-based simulation built in Unity, and a Phantom Omni / Geomagic Touch haptic device that controls the virtual robot's end-effector and provides force feedback. Through user studies, we show that the use of this tool can significantly improve a user's estimation of the suitability of a design for robot operation, in two case studies involving replacing a unit in a modular hardware system and unzipping a canvas bag. By incorporating the use of such a tool early in the design cycle, designers can more effectively develop equipment to be used by autonomous robots without themselves needing direct robotics experience; as a result, robots will be able to take on more tasks in the nearer term with current robot technology.

High aspect ratios are a common feature in biological systems like muscle fibers, tentacles, or annelids that inspire novel applications in artificial muscles, grasping, manipulation, and locomotion. This paper explores interoceptive and exteroceptive sensing methods for high-aspect-ratio soft robots to overcome the limitation of externalized sensing and control, which is currently typical for such robots. We present a design and manufacturing process for sensorized soft robots (aspect ratio ∼17) with an integrated stretchable carbon microparticle proprioception sensor and phototransistor-based exteroceptive layer for low-resolution ambient light detection. We show that our interoceptive sensor provides accurate results for curling during 120 pressurization cycles. The exteroceptive sensor detects the proximity of other robots but shows only a slight correlation during entanglement tests. Finally, we demonstrate that sensorized high-aspect-ratio soft robots can detect the disentanglement of robots under load.

Beavers are famous for building extensive damming complexes 1 , which can extend over kilometer-scale distances 2 and persist for centuries 3 . Dams have major impacts on their surrounding environment, including effects on the hydrology, geomorphology, and ecosystems of the area 4 . Current understanding of how the environment in turn affects beavers’ building activity centers on the single auditory cue of running water, based primarily on captive studies 14,15 . Observational challenges have limited a more detailed picture of the full feedback loop between beavers and their environment. Here we describe the detailed progress of new construction by 20 beaver colonies in northwest Montana, through field studies using drones to obtain surveys with spatial and temporal resolution each three orders of magnitude finer than typically reported 5-13 . We show that both the timing and location of beaver construction activity are influenced by environmental factors. Initiation of trail clearing, a stage preceding dam building, was associated with a narrow range of stream flow rates. Beavers preferentially built in locations associated with preexisting canals. These results emphasize the importance of non-dam elements in the beavers’ construction and its coordination across the colony, and point to environmental feedback processes that may span across years and unrelated colonies.

Gripper hardware design often involves a trade-off between distinct and sometimes opposing goals (e.g., high grasping force vs. gentleness). To address this trade-off within a single device, we present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-mode</i> gripper with fingers that are scissor linkages, that can actively transform between three distinct modes by varying the number and locations of mechanical singularities. Each of these modes has properties that are suitable for specific needs. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 1</small> provides high grip strength, using a short lever arm and rigid structure. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 2</small> allows precise finger positioning and in-hand manipulation, using two independently controlled DOFs per finger. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 3</small> provides underactuated grasping that can passively adapt to delicate or irregularly shaped objects, with four DOFs per finger. The kinematic relationships, joint torques, and fingertip forces are derived analytically for each of the three modes. Gripper performance and the kinematic model are verified experimentally.