We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the wild. Our framework generalizes the benefits of series elasticity to 6-dof, while providing an abstraction of control using visuotactile sensing. We propose an algorithm for relative pose estimation from visuotactile sensing, and a spatial hybrid force-position controller capable of achieving stable force interaction with the environment. We demonstrate the effectiveness of our framework on tools that require regulation of spatial forces. Video link: https://youtu.be/2-YuIfspDrk.

Manipulation in cluttered environments like homes requires stable grasps, precise placement and robustness against external contact. Towards addressing these challenges, we present the Soft-bubble gripper system that combines highly compliant gripping surfaces with dense-geometry visuotactile sensing and facilitates multiple kinds of tactile perception. We first present several mechanical design advances on the Soft-bubble sensors including a fabrication technique to deposit custom patterns to the internal surface of the sensor membrane that enables tracking of shear-induced displacement of the grasped object. The depth maps output by the internal imaging sensor are used in an in-hand proximity pose estimation framework - the method better captures distances to corners or edges on the object geometry. We also extend our previous work on tactile classification and integrate the system within a robust manipulation pipeline for cluttered home environments. The capabilities of the proposed system are demonstrated through robust execution of multiple real-world manipulation tasks.

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 develop a reconfigurable legged robot, named Snapbot, to emulate configuration changes and various styles of legged locomotion. The body of Snapbot houses a microcontroller and a battery for untethered operation. The body also contains connections for communication and power to the modular legs. The legs can be attached to and detached from the body using magnetic mechanical couplings. In the center of this coupling, there is a multi-pin spring-loaded electrical connector that distributes power and transmits data between the controller and leg actuators. The locomotion algorithm is implemented on the microcontroller. The algorithm enables Snapbot to locomote in various configurations with one to six legs by recognizing configuration changes and selecting the locomotion method according to the current configuration. Snapbot will be utilized for further research on legged locomotion.

The design of legged robots is often inspired by animals evolved to excel at different tasks. However, while mimicking morphological features seen in nature can be very powerful, robots may need to perform motor tasks that their living counterparts do not. In the absence of designs that can be mimicked, an alternative is to resort to mathematical models that allow the relationship between a robot's form and function to be explored. In this paper, we propose such a model to co-design the motion and leg configurations of a robot such that a measure of performance is optimized. The framework begins by planning trajectories for a simplified model consisting of the center of mass and feet. The framework then optimizes the length of each leg link while solving for associated full-body motions. Our model was successfully used to find optimized designs for legged robots performing tasks that include jumping, walking, and climbing up a step. Although our results are preliminary and our analysis makes a number of simplifying assumptions, our findings indicate that the cost function, the sum of squared joint torques over the duration of a task, varies substantially as the design parameters change.

Effective teleoperation requires real-time control of a remote robotic system. In this work, we develop a controller for realizing smooth and accurate motion of a robotic head with application to a teleoperation system for the Furhat robot head [1], which we call TeleFurhat. The controller uses the head motion of an operator measured by a Microsoft Kinect 2 sensor as reference and applies a processing framework to condition and render the motion on the robot head. The processing framework includes a pre-filter based on a moving average filter, a neural network-based model for improving the accuracy of the raw pose measurements of Kinect, and a constrained-state Kalman filter that uses a minimum jerk model to smooth motion trajectories and limit the magnitude of changes in position, velocity, and acceleration. Our results demonstrate that the robot can reproduce the human head motion in real time with a latency of approximately 100 to 170 ms while operating within its physical limits. Furthermore, viewers prefer our new method over rendering the raw pose data from Kinect.

We have developed a toy sized humanoid robot with soft air-filled modules on its links which sense contact and protect the robot and any interacting humans from damaging collisions. This robot, meant for robust physical interaction, is required to endure contact with children in the form of hugs and other playful interactions. It is therefore necessary to quantify the forces exerted during these interactions so that robots can be designed to both withstand these forces, as well as interact safely and intuitively in these situations. To quantify the range of forces exerted by children when performing both soft and strong hugs, we conducted a study in which 28 children (11 boys, 17 girls) between 4 and 10 years old hugged a pressure sensing doll while the pressure was recorded. We found a child's maximum expected hugging force (2.623 psi for our setup) during free play. The data gathered in this study will guide the further development of our physically interactive robot.

The purpose of this research is the development of a soft skin module with a built-in airtight cavity in which air pressure can be sensed. A pressure feedback controller is implemented on a robotic system using this module for contact sensing and gentle grasping. The soft skin module is designed to meet size and safety criteria appropriate for a toy-sized interactive robot. All module prototypes are produced using a muti-material 3D printer. Experimental results from collision tests show that this module significantly reduces the impact forces due to collision. Also, using the measured pressure information from the module, the robotic system to which these modules are attached is capable of very gentle physical interaction with soft objects.