We show how a variety of techniques from Computer Graphics can be leveraged to intuitively control the shape (configuration) of arbitrary 3D Soft Robots in VR. Our pipeline, Virtual Reality Soft Robot Inverse Kinematics (VR-Soft IK), overcomes fundamental limitations of general-purpose drag-and-drop soft robot control interfaces by leaving the 2D computer screen for 3D Virtual Reality (VR). VR-Soft IK uses a simulation based on the Finite Element Method (FEM) and a control method based on sensitivity analysis. Additionally, we show that our general control pipeline can be fused with techniques from 3D character animation to skin our simulation with a high-resolution surface mesh, pointing a way toward Mixed Reality Soft Robots. This full Skinned VR-Soft IK pipeline uses skeletal animation and GPU picking. We demonstrate the utility of our pipeline by doing real-time, open-loop control of the real-world 3D soft robotic arm Helix.

We explore the idea of robotic mechanisms that can shift between soft and rigid states, with the long-term goal of creating robots that marry the flexibility and robustness of soft robots with the strength and precision of rigid robots. We present a simple yet effective method to achieve large and rapid stiffness variations by compressing and relaxing a flexure using cables. Next, we provide a differentiable modeling framework that can be used for motion planning, which simultaneously reasons about the modulated stiffness joints, tendons, rigid joints, and basic hydrodynamics. We apply this stiffness tuning and simulation recipe to create SoRiTu, an untethered soft-rigid robotic sea turtle capable of various swimming maneuvers.

Soft robots can be incredibly robust and safe but typically fail to match the strength and precision of rigid robots. This dichotomy between soft and rigid is recently starting to break down, with emerging research interest in hybrid soft-rigid robots. In this work, we draw inspiration from Nature, which achieves the best of both worlds by coupling soft and rigid tissues-like muscle and bone-to produce biological systems capable of both robustness and strength. We present foundational, general-purpose pipelines to simulate and fabricate cable-driven soft-rigid robots with embedded skeletons. We show that robots built using these methods can fluidly mimic biological systems while achieving greater force output and external load resistance than purely soft robots. Finally, we show how our simulation and fabrication pipelines can be leveraged to create more complex robots and do model-based control.

This paper outlines the design, specifications, and algorithms for a new modular self-reconfigurable robotic system; at its foundation is a novel modular magnetically geared linear actuator paired with a kinematic coupling connector. Motivating this work is the core idea that high performance actuators as well as inexpensive, precise and repeatable connectors are the key ingredients required for useful real-world self-reconfiguring machines. This work builds upon existing research in the areas of modular self-reconfigurable robots, magnetic lead screws, modular machine tools and kinematic couplings. Magnetic lead screws (MLS) have many desirable characteristics applicable to modular robots, including a tolerance for slight misalignments, high efficiency, zero backlash, robustness, inherent series elasticity, high force capability, and the ability to gracefully separate and reattach. Due to their high mechanical efficiency, MLS actuators are able to be combined in parallel to provide for increased forces and stiffness. Our system implements a MLS through two separable elements: brushless motor powered actuators called carts which pair with modular passive tracks which constrain the carts' movement to a line. This paper also explores the design for a connector which is able to precisely align modules through the use of a 4-way symmetric kinematic coupling.

This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way, we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures, including a demo application in soft robotics.

We present a computational framework for robotic animation of real-world string puppets. Also known as marionettes, these articulated figures are typically brought to life by human puppeteers. The puppeteer manipulates rigid handles that are attached to the puppet from above via strings. The motions of the marionette are therefore governed largely by gravity, the pull forces exerted by the strings, and the internal forces arising from mechanical articulation constraints. This seemingly simple setup conceals a very challenging and nuanced control problem, as marionettes are, in fact, complex coupled pendulum systems. Despite this, in the hands of a master puppeteer, marionette animation can be nothing short of mesmerizing. Our goal is to enable autonomous robots to animate marionettes with a level of skill that approaches that of human puppeteers. To this end, we devise a predictive control model that accounts for the dynamics of the marionette and kinematics of the robot puppeteer. The input to our system consists of a string puppet design and a target motion, and our trajectory planning algorithm computes robot control actions that lead to the marionette moving as desired. We validate our methodology through a series of experiments conducted on an array of marionette designs and target motions. These experiments are performed both in simulation and using a physical robot, the human-sized, dual arm ABB YuMi ® IRB 14000.

In this paper, we address the challenge of robotic manipulation of elastically deforming objects. To this end, we model elastic objects using the Finite Element Method. Through a quasi-static assumption, we leverage sensitivity analysis to mathematically model how changes in the robot's configuration affect the deformed shape of the object being manipulated. This enables an interactive, simulation-based control methodology, wherein user-specified deformations for the elastic objects are automatically mapped to joint angle commands. The optimization formulation we introduce is general, operates directly within a robot's workspace and can readily incorporate joint limits as well as collision avoidance between the links. We validate our control methodology on a YuMi® IRB 14000, which we use to manipulate a variety of elastic objects.

We present a novel computational approach to design the robotic devices from high-level motion specifications. Our computational system uses a library of modular components-actuators, mounting brackets, and connectors-to define the space of possible robot designs. The process of creating a new robot begins with a set of input trajectories that specify how its end effectors and/or body should move. By searching through the combinatorial set of possible arrangements of modular components, our method generates a functional, as-simple-as-possible robotic device that is capable of tracking the input motion trajectories. To significantly improve the efficiency of this discrete optimization process, we propose a novel heuristic that guides the search for appropriate designs. Briefly, our heuristic function estimates how much an intermediate robot design needs to change before it becomes able to execute the target motion trajectories. We demonstrate the effectiveness of our computational design method by automatically creating a variety of robotic manipulators and legged robots. To generate these results, we define our own robotic kit that includes off-the-shelf actuators and 3-D printable connectors. We validate our results by fabricating two robotic devices designed with our method.

We present a class of tendon-actuated soft robots, which promise to be low-cost and accessible to non-experts. The fabrication techniques we introduce are largely based on traditional techniques for fabricating plush toys, and so we term the robots created using our approach “plush robots.” A plush robot moves by driving internal winches that pull in (or let out) tendons routed through its skin. We provide a forward simulation model for predicting a plush robot's deformation behavior given some contractions of its internal winches. We also leverage this forward model for use in an interactive control scheme, in which the user provides a target pose for the robot, and optimal contractions of the robot's winches are automatically computed in real-time. We fabricate two examples to demonstrate the use of our system, and also discuss the design challenges inherent to plush robots.

We present a computational approach to creating animated plushies, soft robotic plush toys specifically-designed to reenact user-authored motions. Our design process is inspired by muscular hydrostat structures, which drive highly versatile motions in many biological systems. We begin by instrumenting simulated plush toys with a large number of small, independently-actuated, virtual muscle-fibers. Through an intuitive posing interface, users then begin animating their plushie. A novel numerical solver, reminiscent of inverse-kinematics, computes optimal contractions for each muscle-fiber such that the soft body of the plushie deforms to best match user input. By analyzing the co-activation patterns of the fibers that contribute most to the plushie's motions, our design system generates physically-realizable winch-tendon networks. Winch-tendon networks model the motorized cable-driven actuation mechanisms that drive the motions of our real-life plush toy prototypes. We demonstrate the effectiveness of our computational approach by co-designing motions and actuation systems for a variety of physically-simulated and fabricated plushies.

In this paper, we present algorithms for self-reconfiguration of modular robots that move by pivoting. The modules are cubes that can pivot about their edges along the x̂, ŷ, or ẑ axes to move on a 3-dimensional substrate. This is a different model from prior work, which usually considers modules that slide along their faces. We analyze the pivoting cube model and give sufficient conditions for reconfiguration to be feasible. In particular, we show that if an initial configuration does not contain any of three subconfigurations, which we call rules, then it can reconfigure into a line. We provide provably correct algorithms for reconfiguration for both 2-D and 3-D systems, and we verify our algorithms via simulation on randomly generated 2-D and 3-D configurations.