Talk 1 : Challenges in Extending Sampling Based Motion Planning to the Stochastic Setting
Speaker : Lucas Janson
Abstract :
Sampling Based Motion Planning algorithms such as RRT* and PRM* provide computationally tractable ways to plan trajectories around complicated obstacles in high dimensions. Most of the theoretical properties for these algorithms, such as probabilistic feasibility and asymptotic optimality, has been mainly developed for the deterministic case in which a robot has perfect knowledge of its state and environment at all times. I will discuss the challenges for extending similar results and algorithms to the more realistic stochastic setting, when the robot has some motion uncertainty and a noisy sensor to position itself, and wants a cost-optimal trajectory while maintaining a low probability of collision. For example, from a computational standpoint, path collision probabilities need to be computed quickly and current approximations are extremely crude, and from an optimization standpoint, solving the chance-constrained problem formulation is much more challenging than just the collision-free formulation.


Talk 2 : Hapkit: A low-cost educational haptic device 
Speaker : Tania Morimoto
Abstract : 
We have designed a low-cost, open-hardware haptic device, called Hapkit, in order to provide a hands-on laboratory experience in an introductory online haptics course. Hapkit is a one-degree-of-freedom kinesthetic haptic device that allows users to input motions and feel programmed forces. We piloted Hapkit in an online course in Autumn 2013, and it has since been used in an introductory controls class as well as graduate-level haptics class. The part files, assembly instructions, and template code are all open-source and can be found at http://hapkit.stanford.edu/.


Talk 3 : A dynamical system approach to Learning robot motions from human demonstrations
Speaker : Mohammad Khansari
Abstract :
The ability to react autonomously and robustly to dynamically changing environments is an essential feature for robots that are envisioned to work in human environments. In my talk, I will present a method called Stable Estimator of Dynamical Systems (SEDS); a framework that allows for fast learning of robot reaching motions from a small set of demonstrations. SEDS has four main features: 1) it can produce human-like movements, 2) it has guaranteed global asymptotic stability at the target point (if the target is reachable), 3) it is inherently robust to perturbations, and 4) it can instantly adapt to changes in dynamic environments. I will showcase performance of SEDS in a number of robot experiments, including reaching a target in a dynamic environment, playing mini-golf, dodging fast moving objects, and catching flying objects.


Talk 4 : 3D Ultrasound with a Single Transducer
Speaker : Mark Stauber
Abstract :
A radiotranslucent, single-transducer 3D ultrasound probe for volumetric imaging would address challenges in both imaging and guidance. Opportunities for such a device are increasing, particularly with the growing potential of targeted contrast-enhanced ultrasound in cancer detection. In the Salisbury Robotics Lab we have developed a method for single-transducer 3D ultrasound that leverages simple mechanical design and advanced imaging processing to meet these needs. The probe has applications for robotically guided radiation therapy and also represents a low-cost alternative to current methods of volumetric ultrasound.