While previously proposed hardware on pin-based shape display has improved various technical aspects, there has been a clear limitation on the haptic quality of variable 'force' feedback. In this paper, we explore a novel haptic interaction design space with 'force' controlled shape display. Utilizing high performance linear actuators with current reading functionality, we built a 10 x 5 'force' shape display, named inFORCE, that can both detect and exert variable force on individual pins. By integrating closed-loop force control, our system can provide real-time variable haptic feedback in response to the way users press the pins. Our haptic interaction design space includes volumetric haptic feedback, material emulation, layer snapping, and friction. Our proposed interaction methods, for example, enables people to "press through'' computationally rendered dynamic shapes to understand the internal structure of 3D volumetric information. We also demonstrate a material property capturing functionality. Our technical evaluation and user study assesses the hardware capability and haptic perception through interaction with inFORCE. We also discuss application spaces that 'force' shape display can be used for.

We developed a concept of transformative appetite, where edible 2D films made of common food materials (protein, cellulose or starch) can transform into 3D food during cooking. This transformation process is triggered by water adsorption, and it is strongly compatible with the 'flat packaging' concept for substantially reducing shipping costs and storage space. To develop these transformable foods, we performed material-based design, established a hybrid fabrication strategy, and conducted performance simulation. Users can customize food shape transformations through a pre-defined simulation platform, and then fabricate these designed patterns using additive manufacturing. Three application techniques are provided - 2D-to-3D folding, hydration-induced wrapping, and temperature-induced self-fragmentation, to present the shape, texture, and interaction with food materials. Based on this concept, several dishes were created in the kitchen, to demonstrate the futuristic dining experience through materials-based interaction design.

We consider the sensor selection problem on multivariate Gaussian distributions where only a \emph{subset} of latent variables is of inferential interest. For pairs of vertices connected by a unique path in the graph, we show that there exist decompositions of nonlocal mutual information into local information measures that can be computed efficiently from the output of message passing algorithms. We integrate these decompositions into a computationally efficient greedy selector where the computational expense of quantification can be distributed across nodes in the network. Experimental results demonstrate the comparative efficiency of our algorithms for sensor selection in high-dimensional distributions. We additionally derive an online-computable performance bound based on augmentations of the relevant latent variable set that, when such a valid augmentation exists, is applicable for \emph{any} distribution with nuisances.

This paper considers the path planning problem for an autonomous vehicle in an urban environment populated with static obstacles and moving vehicles with uncertain intents. We propose a novel threat assessment module, consisting of an intention predictor and a threat assessor, which augments the host vehicle's path planner with a real-time threat value representing the risks posed by the estimated intentions of other vehicles. This new threat-aware planning approach is applied to the CL-RRT path planning framework, used by the MIT team in the 2007 DARPA Grand Challenge. The strengths of this approach are demonstrated through simulation and experiments performed in the RAVEN testbed facilities.

This paper describes the system architecture and core algorithms for a quadrotor helicopter that uses vision data to navigate an unknown, indoor, GPS-denied environment. Without external sensing, an estimation system that relies only on integrating inertial data will have rapidly drifting position estimates. Micro aerial vehicles (MAVs) are stringently weight-constrained, leaving little margin for additional sensors beyond the mission payload. The approach taken in this paper is to introduce an architecture that exploits a common mission payload, namely a video camera, as a dual-use sensor to aid in navigation. Several core algorithms, including a fast environment mapper and a novel heuristic for obstacle avoidance, are also presented. Finally, drift-free hover and obstacle avoidance flight tests in a controlled environment are presented and analyzed.