Current Virtual Reality (VR) environments lack the rich haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The result of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need for learning a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.

Today's robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one.

This study evaluates the accuracy of three different types of time-of-flight sensors to measure distance. We envision the possible use of these sensors to localize swarms of flying light specks (FLSs) to illuminate objects and avatars of a metaverse. An FLS is a miniature-sized drone configured with RGB light sources. It is unable to illuminate a point cloud by itself. However, the inter-FLS relationship effect of an organizational framework will compensate for the simplicity of each individual FLS, enabling a swarm of cooperating FLSs to illuminate complex shapes and render haptic interactions. Distance between FLSs is an important criterion of the inter-FLS relationship. We consider sensors that use radio frequency (UWB), infrared light (IR), and sound (ultrasonic) to quantify this metric. Obtained results show only one sensor is able to measure distances as small as 1 cm with a high accuracy. A sensor may require a calibration process that impacts its accuracy in measuring distance.

Perception in robot manipulation has been actively explored with the goal of advancing and integrating vision and touch for global and local feature extraction. However, it is difficult to perceive certain object internal states, and the integration of visual and haptic perception is not compact and is easily biased. We propose to address these limitations by developing an active acoustic sensing method for robot manipulation. Active acoustic sensing relies on the resonant properties of the object, which are related to its material, shape, internal structure, and contact interactions with the gripper and environment. The sensor consists of a vibration actuator paired with a piezo-electric microphone. The actuator generates a waveform, and the microphone tracks the waveform's propagation and distortion as it travels through the object. This paper presents the sensing principles, hardware design, simulation development, and evaluation of physical and simulated sensory data under different conditions as a proof-of-concept. This work aims to provide fundamentals on a useful tool for downstream robot manipulation tasks using active acoustic sensing, such as object recognition, grasping point estimation, object pose estimation, and external contact formation detection.