Arman Tekinalp

A long-standing engineering problem, the control of soft robots is difficult because of their highly nonlinear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, neural reservoirs are employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across challenging tasks, outperforming classic neural network approaches. Further, through the use of spiking reservoirs on neuromorphic hardware, energy efficiency gains of up to 75 and 45 times are obtained relative to standard and high-efficiency CPUs, with implications for the on-board control of untethered, small-scale systems.

Muscular hydrostats, such as octopus arms or elephant trunks, lack bones entirely, endowing them with exceptional dexterity and reconfigurability. Key to their unmatched ability to control nearly infinite degrees of freedom is the architecture into which muscle fibers are weaved. Their arrangement is, effectively, the instantiation of a sophisticated mechanical program that mediates, and likely facilitates, the control and realization of complex, dynamic morphological reconfigurations. Here, by combining medical imaging, biomechanical data, live behavioral experiments, and numerical simulations, an octopus-inspired arm made of ∼ 200 continuous muscle groups is synthesized, exposing “mechanically intelligent” design and control principles broadly pertinent to dynamics and robotics. Such principles are mathematically understood in terms of storage, transport, and conversion of topological quantities, effected into complex 3D motions via simple muscle activation templates. These are in turn composed into higher-level control strategies that, compounded by the arm’s compliance, are demonstrated across challenging manipulation tasks, revealing surprising simplicity and robustness.

By masterfully balancing directed growth and passive mechanics, plant roots are remarkably capable of navigating complex heterogeneous environments to find resources. Here we present a theoretical and numerical framework which allows us to interrogate and simulate the mechanical impact of solid interfaces on the growth pattern of plant organs. We focus on the well-known waving, coiling and skewing patterns exhibited by roots of Arabidopsis thaliana when grown on inclined surfaces, serving as a minimal model of the intricate interplay with solid substrates. By modelling growing slender organs as Cosserat rods that mechanically interact with the environment, our simulations verify hypotheses of waving and coiling arising from the combination of active gravitropism and passive root-plane responses. Skewing is instead related to intrinsic twist due to cell file rotation. Numerical investigations are outfitted with an analytical framework that consistently relates transitions between straight, waving, coiling and skewing patterns with substrate tilt angle. Simulations are found to corroborate theory and recapitulate a host of reported experimental observations, thus providing a systematic approach for studying in silico plant organs behavior in relation to their environment. Significance: Plant roots exhibit an exceptional ability to navigate in heterogeneous soil environments while overcoming obstacles. Our study combines theory and experimental observations to interrogate and simulate the mechanical impact of obstacles on organ growth. As a test case we focus on well-known observations of waving, coiling and skewing growth patterns of Arabidopsis thaliana roots grown on inclined substrates. Overall, our study explains a broad set of experimental observations through the minimal ingredients of gravitropism and passive mechanics. Our numerical framework provides an in silico laboratory, yielding quantitative insight into the dynamics of growing organs at the intersection of active processes and passive mechanics, applicable beyond plants to any slender growing system, from neurons or fungal hyphae to novel soft robots.

Soft robots are notoriously hard to control. This is partly due to the scarcity of models and simulators able to capture their complex continuum mechanics, resulting in a lack of control methodologies that take full advantage of body compliance. Currently available methods are either too computational demanding or overly simplistic in their physical assumptions, leading to a paucity of available simulation resources for developing such control schemes. To address this, we introduce Elastica, an open-source simulation environment modeling the dynamics of soft, slender rods that can bend, twist, shear, and stretch. We couple Elastica with five state-of-the-art reinforcement learning (RL) algorithms (TRPO, PPO, DDPG, TD3, and SAC). We successfully demonstrate distributed, dynamic control of a soft robotic arm in four scenarios with both large action spaces, where RL learning is difficult, and small action spaces, where the RL actor must learn to interact with its environment. Training converges in 10 million policy evaluations with near real-time evaluation of learned policies.