Alexander Alspach

3D printed soft skin for safe human-robot interaction

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.

Joint Optimization of Robot Design and Motion Parameters using the Implicit Function Theorem

We present a novel computational approach to optimizing the morphological design of robots. Our framework takes as input a parameterized robot design and a motion plan consisting of trajectories for end-effectors, as well as optionally, for its body. The algorithm we propose is used to optimize design parameters, namely link lengths and the placement of actuators, while concurrently adjusting motion parameters such as joint trajectories, actuator inputs, and contact forces. Our key insight is that the complex relationship between design and motion parameters can be established via sensitivity analysis if the robot’s movements are modeled as spatio-temporal solutions to optimal control problems. This relationship between form and function allows us to automatically optimize robot designs based on specifications expressed as a function of range of motion or actuator forces. We evaluate our model by computationally optimizing two simulated robots that employ linear actuators: a manipulator and a large quadruped. We further validate our framework by optimizing the design of a small quadrupedal robot and testing its performance using a hardware implementation.

Task-based limb optimization for legged robots

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 robots 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.

Imitating human movement with teleoperated robotic head

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.

Computational co-optimization of design parameters and motion trajectories for robotic systems:

We present a novel computational approach to optimizing the morphological design of robots. Our framework takes as input a parameterized robot design as well as a motion plan consisting of trajectories for end-effectors and, optionally, for its body. The algorithm optimizes the design parameters including link lengths and actuator placements whereas concurrently adjusting motion parameters such as joint trajectories, actuator inputs, and contact forces. Our key insight is that the complex relationship between design and motion parameters can be established via sensitivity analysis if the robot’s movements are modeled as spatiotemporal solutions to an optimal control problem. This relationship between form and function allows us to automatically optimize the robot design based on specifications expressed as a function of actuator forces or trajectories. We evaluate our model by computationally optimizing four simulated robots that employ linear actuators, four-bar linkages, or rotary servos. We further validate our framework by optimizing the design of two small quadruped robots and testing their performances using hardware implementations.

Study of children's hugging for interactive robot design

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 childs 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.

Mechanical implementation of a variable-stiffness actuator for a softly strummed ukulele

This research illustrates the design, implementation, and evaluation of pneumatic variable-stiffness actuator (VSA) used to strum a four-stringed ukulele with audio variability. A guitar pick is antagonistically loaded with two inflatable polydimethylsiloxane (PDMS) actuators, allowing for the independent control of both position or stiffness through the utilization of one and two PDMS solenoids, respectively. To generate smooth analog pressure signals, the bellows incorporate a controlled leak to atmospheric pressure, having synonymous properties to a low-pass filter circuit when fed a coarse pressure signal through PWM control. Experimental results illustrate a minimum to maximum stiffness range of 76 to 320 Nmm/rad, a maximum cyclical speeds of 2.04 Hz, and a stiffness change rate of 96 Nmm/rad/kPa. Additional to the VSA, alternative PDMS fabrication methods are presented to allow for complex, precise manufacturing of silicone bodies in a low-cost manner.

Analyzing Muscle Activity and Force with Skin Shape Captured by Non-contact Visual Sensor

Estimating physical information by vision as humans do is useful for the applications with physical interaction in the real world. For example, observing muscle bulging infers how much force a person puts on the muscle to interact with an object or environment. Since the human skin deforms due to muscle activity, it is expected that skin deformation gives information to analyze human motion. This paper demonstrates that biomechanical information can be derived from skin shape by analyzing the relationship between skin deformation, force produced by muscles, and muscle activity. We first obtained the dataset simultaneously acquired by a range sensor, a force sensor, and electromyograph EMG sensors. Since recent range sensors based on non-contact visual measurement acquires accurate and dense shape of an object at high frame rate, the deforming skin can be observed. The deformation is calculated by finding the correspondence between a template shape and each range scan. The relationship between skin deformation and other data is learned. In this paper, the following problems are considered: 1 estimating force from skin shape, 2 estimating muscle activity from skin shape, 3 synthesizing skin shape from muscle activity. In the experiments, the database learned from the sensor data can be used for the above problems, and the skin shape gives useful information to explain the muscle activity.