Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

Millirobots are a promising robotic platform for many applications due to their small size and low manufacturing costs. Legged millirobots, in particular, can provide increased mobility in complex environments and improved scaling of obstacles. However, controlling these small, highly dynamic, and underactuated legged systems is difficult. Hand-engineered controllers can sometimes control these legged millirobots, but they have difficulties with dynamic maneuvers and complex terrains. We present an approach for controlling a real-world legged millirobot that is based on learned neural network models. Using less than 17 minutes of data, our method can learn a predictive model of the robot's dynamics that can enable effective gaits to be synthesized on the fly for following user-specified waypoints on a given terrain. Furthermore, by leveraging expressive, high-capacity neural network models, our approach allows for these predictions to be directly conditioned on camera images, endowing the robot with the ability to predict how different terrains might affect its dynamics. This enables sample-efficient and effective learning for locomotion of a dynamic legged millirobot on various terrains, including gravel, turf, carpet, and styrofoam. Experiment videos can be found at https://sites.google.com/view/imageconddy

Functional Soft Robotic Actuators Based on Dielectric Elastomers

Dielectric elastomer actuators (DEAs) are a promising soft actuator technology for robotics. Adding robotic functionalities--folding, variable stiffness, and adhesion--into their actuator design is a novel method to create functionalized robots with simplified actuator configurations. We first propose a foldable actuator that has a simple antagonistic DEA configuration allowing bidirectional actuation and passive folding. To prove the concept, a foldable elevon actuator with outline size of 70 mm × 130 mm is developed with a performance specification matched to a 400 mm wingspan micro air vehicle (MAV) of mass 130 g. The developed actuator exhibits actuation angles up to ± 26 ° and a torque of 2720 mN·mm in good agreement with a prediction model. During a flight, two of these integrated elevon actuators well controlled the MAV, as proven by a strong correlation of 0.7 between the control signal and the MAV motion. We next propose a variable stiffness actuator consisting of a pre-stretched DEA bonded on a low-melting-point alloy (LMPA) embedded silicone substrate. The phase of the LMPA changes between liquid and solid enabling variable stiffness of the structure, between soft and rigid states, while the DEA generates a bending actuation. A proof-of-concept actuator with dimension 40 mm length × 10mm width × 1mm thickness and a mass of 1 g is fabricated and characterized. Actuation is observed up to 47.5 ° angle and yielding up to 2.4 mN of force in the soft state. The stiffness in the rigid state is ~90 × larger than an actuator without LMPA. We develop a two-finger gripper in which the actuators act as the fingers. The rigid state allows picking up an object mass of 11 g (108 mN), to be picked up even though the actuated grasping force is only 2.4 mN. We finally propose an electroadhesion actuator that has a DEA design simultaneously maximizing electroadhesion and electrostatic actuation, while allowing self-sensing by employing an interdigitated electrode geometry. The concept is validated through development of a two-finger soft gripper, and experimental samples are characterized to address an optimal design. We observe that the proposed DEA design generates 10 × larger electroadhesion force compared to a conventional DEA design, equating to a gripper with a high holding force (3.5 N shear force for 1 cm^2) yet a low grasping force (1 mN). These features make the developed simple gripper to handle a wide range of challenging objects such as highly-deformable water balloons (35.6 g), flat paper (0.8 g), and a raw chicken egg (60.9 g), with its lightweight (1.5 g) and fast movement (100 ms to close fingers). The results in this thesis address the creation of the functionalized robots and expanding the use of DEAs in robotics