Ruta Desai

3D Printing Pneumatic Device Controls with Variable Activation Force Capabilities

We explore 3D printing physical controls whose tactile response can be manipulated programmatically through pneumatic actuation. In particular, by manipulating the internal air pressure of various pneumatic elements, we can create mechanisms that require different levels of actuation force and can also change their shape. We introduce and discuss a series of example 3D printed pneumatic controls, which demonstrate the feasibility of our approach. This includes conventional controls, such as buttons, knobs and sliders, but also extends to domains such as toys and deformable interfaces. We describe the challenges that we faced and the methods that we used to overcome some of the limitations of current 3D printing technology. We conclude with example applications and thoughts on future avenues of research.

Muscle-reflex control of robust swing leg placement

Swing leg placement is vital to dynamic stability in legged robots and animals. The most common approaches to generating swing leg motions in robotics use either position or impedance tracking of defined joint trajectories. While these approaches suffice in humanoids, they severely limit swing leg placement under large disturbances in prosthetic limbs, for which stabilizing reactions cannot be planned centrally. We previously identified a control for a conceptual double-pendulum swing leg that does not require predefined trajectories to robustly place the leg into target points on the ground in the presence of large disturbances. We here develop a more detailed neuromuscular model of the human swing leg and interpret the identified control with local muscle reflexes. The resulting reflex control robustly places the swing leg into a wide range of landing points observed in human walking and running, and it generates similar patterns of joint torques and muscle activations. While we plan to take advantage of the comparison between the neuromuscular model and humans to further improve robust swing leg placement, the current results suggest an alternative to existing swing leg controls in humanoid and rehabilitation robotics which does not require central processing.

Robust swing leg placement under large disturbances

Swing leg placement is vital to dynamic stability in legged robots and animals. The most common approaches to generating swing leg motions in robotics use either position or impedance tracking of defined joint trajectories. While these approaches suffice in humanoids, they severely limit swing leg placement under large disturbances in prosthetic limbs, for which stabilizing reactions cannot be planned centrally. Rather than careful central planning, animals and humans seem to rely on local feedback control for reliable swing leg placement. Motivated by this observation, we here present an alternative for generating swing leg motions. We develop a local swing leg control that takes advantage of segment interactions to achieve robust leg placement under large disturbances while generating trajectories and joint torque patterns similar to those patterns observed in human walking and running. The results suggest the identified control as a powerful alternative to existing swing leg controls in humanoid and rehabilitation robotics.

Integration of an adaptive swing control into a neuromuscular human walking model

Understanding the neuromuscular control underlying human locomotion has the potential to deliver practical controllers for humanoid and prosthetic robots. However, neurocontrollers developed in forward dynamic simulations are seldom applied as practical controllers due to their lack of robustness and adaptability. A key element for robust and adaptive locomotion is swing leg placement. Here we integrate a previously identified robust swing leg controller into a full neuromuscular human walking model and demonstrate that the integrated model has largely improved behaviors including walking on very rough terrain (±10cm) and stair climbing (15cm stairs). These initial results highlight the potential of the identified robust swing control. We plan to generalize it to a range of human locomotion behaviors critical in rehabilitation robotics.

Computational abstractions for interactive design of robotic devices

We present a computational design system that allows novices and experts alike to easily create custom robotic devices using modular electromechanical components. The core of our work consists of a design abstraction that models the way in which these components can be combined to form complex robotic systems. We use this abstraction to develop a visual design environment that enables an intuitive exploration of the space of robots that can be created using a given set of actuators, mounting brackets and 3d-printable components. Our computational system also provides support for design auto-completion operations, which further simplifies the task of creating robotic devices. Once robot designs are finished, they can be tested in physical simulations and iteratively improved until they meet the individual needs of their users. We demonstrate the versatility of our computational design system by creating an assortment of legged and wheeled robotic devices. To test the physical feasibility of our designs, we fabricate a wheeled device equipped with a 5-DOF arm and a quadrupedal robot.

Assembly-aware Design of Printable Electromechanical Devices

From smart toys and household appliances to personal robots, electromechanical devices play an increasingly important role in our daily lives. Rather than relying on gadgets that are mass-produced, our goal is to enable casual users to custom-design such devices based on their own needs and preferences. To this end, we present a computational design system that leverages the power of digital fabrication and the emergence of affordable electronics such as sensors and microcontrollers. The input to our system consists of a 3D representation of the desired devices shape, and a set of user-preferred off-the-shelf components. Based on this input, our method generates an optimized, 3D printable enclosure that can house the required components. To create these designs automatically, we formalize a new spatio-temporal model that captures the entire assembly process, including the placement of the components within the device, mounting structures and attachment strategies, the order in which components must be inserted, and collision-free assembly paths. Using this model as a technical core, we then leverage engineering design guidelines and efficient numerical techniques to optimize device designs. In a user study, which also highlights the challenges of designing such devices, we find our system to be effective in reducing the entry barriers faced by casual users in creating such devices. We further demonstrate the versatility of our approach by designing and fabricating three devices with diverse functionalities.

Skaterbots: Optimization-Based Design and Motion Synthesis for Robotic Creatures with Legs and Wheels

We present a computation-driven approach to design optimization and motion synthesis for robotic creatures that locomote using arbitrary arrangements of legs and wheels. Through an intuitive interface, designers first create unique robots by combining different types of servomotors, 3D printable connectors, wheels and feet in a mix-and-match manner. With the resulting robot as input, a novel trajectory optimization formulation generates walking, rolling, gliding and skating motions. These motions emerge naturally based on the components used to design each individual robot. We exploit the particular structure of our formulation and make targeted simplifications to significantly accelerate the underlying numerical solver without compromising quality. This allows designers to interactively choreograph stable, physically-valid motions that are agile and compelling. We furthermore develop a suite of user-guided, semi-automatic, and fully-automatic optimization tools that enable motion-aware edits of the robots physical structure. We demonstrate the efficacy of our design methodology by creating a diverse array of hybrid legged/wheeled mobile robots which we validate using physics simulation and through fabricated prototypes.

Virtual model control for dynamic lateral balance

Motivated by an interest in human-like controllers for humanoids to increase their social acceptance, we investigate lateral balancing for artistic performances on challenging surfaces. Control design for lateral balancing in humanoids has primarily focused on optimal control techniques. While these techniques generate balancing controllers, it remains unclear whether humans use similar strategies. Here we propose that humans prefer intuitive task-space control for lateral balancing on simple as well as challenging surfaces. We develop a virtual model controller for this task and compare with simulations of a planar model, the resulting balancing behavior against human lateral balancing on flat ground and on a seesaw as an example of a challenging surface. We find that the proposed controller can be tuned to respond to balance disturbances on flat ground in a human-like way, and that it mimics human behavior on a seesaw including the failure to stabilize the board, even though an optimal LQR controller is capable of stabilizing it. The results support the hypothesis that humans prefer intuitive control in lateral balancing and suggest that state-of-the-art control approaches in robotics may go beyond what humans can accomplish. These limitations should be taken into account when designing human-like controllers for humanoids.