3D Printed Soft Robotic Hand

Soft robotics is an emerging industry, largely dominated by companies which hand mold their actuators. Our team set out to design an entirely 3D printed soft robotic hand, powered by a pneumatic control system which will prove both the capabilities of soft robots and those of 3D printing. Through research, computer aided design, finite element analysis, and experimental testing, a functioning actuator was created capable of a deflection of 2.17” at a maximum pressure input of 15 psi. The single actuator was expanded into a 4 finger gripper and the design was printed and assembled. The created prototype was ultimately able to lift both a 100-gram apple and a 4-gram pill, proving its functionality in two prominent industries: pharmaceutical and food packing

Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

ROBOSIM, a simulator for robotic systems

ROBOSIM, a simulator for robotic systems, was developed by NASA to aid in the rapid prototyping of automation. ROBOSIM has allowed the development of improved robotic systems concepts for both earth-based and proposed on-orbit applications while significantly reducing development costs. In a cooperative effort with an area university, ROBOSIM was further developed for use in the classroom as a safe and cost-effective way of allowing students to study robotic systems. Students have used ROBOSIM to study existing robotic systems and systems which they have designed in the classroom. Since an advanced simulator/trainer of this type is beneficial not only to NASA projects and programs but industry and academia as well, NASA is in the process of developing this technology for wider public use. An update on the simulators's new application areas, the improvements made to the simulator's design, and current efforts to ensure the timely transfer of this technology are presented