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This paper explores the use of a body-worn vibrotactile vest to convey real-time information from robot to operator. Vibrotactile communication could be useful in providing information without compropmising or loading a person's visual or auditory perception. This paper considers applications in Urban Search and Rescue (USAR) scenarios where a human working alongside a robot is likely to be operating in high cognitive load conditions. The focus is on understanding how best to convey information considering different vibrotactile information coding strategies to enhance scene understanding in scenarios where a robot might be operating remotely as a scout. In exploring information representation, this paper introduces Semantic Haptics, using shapes and patterns to represent certain events as if the skin was a screen, and shows how these lead to bettter learnability and interpreation accuracy.

Theory of Mind (ToM), the capacity to comprehend the mental states of distinct individuals, is essential for numerous practical applications. With the development of large language models (LLMs), there is a heated debate about whether they are able to perform ToM tasks. Previous studies have used different tasks and prompts to test the ToM on LLMs and the results are inconsistent: some studies asserted these models are capable of exhibiting ToM, while others suggest the opposite. In this study, We present ToMChallenges, a dataset for comprehensively evaluating the Theory of Mind based on the Sally-Anne and Smarties tests with a diverse set of tasks. In addition, we also propose an auto-grader to streamline the answer evaluation process. We tested three models: davinci, turbo, and gpt-4. Our evaluation results and error analyses show that LLMs have inconsistent behaviors across prompts and tasks. Performing the ToM tasks robustly remains a challenge for the LLMs. In addition, our paper wants to raise awareness in evaluating the ToM in LLMs and we want to invite more discussion on how to design the prompts and tasks for ToM tasks that can better assess the LLMs' ability.

'I'm not sure when the robots will take over but I sure am looking forward to it, I think it will be a lot of fun,' a three-legged humanoid robot tells me. Desdemona, as she's known, then adds with an eerily emotionless tone: 'Maybe this afternoon? DailyMail.com is at Berlin's annual IFA tech show this week, the world's largest and longest-running consumer electronics and home appliances conference. It has been on the go since 1924 and is where the first television were launched - how far we've come in less than 100 years! To take my mind off my slightly unnerving conversation with Desdemona, I go over to the video game section, where I'm told to try on a high-tech vest.

Novel view synthesis (NVS) and video prediction (VP) are typically considered disjoint tasks in computer vision. However, they can both be seen as ways to observe the spatial-temporal world: NVS aims to synthesize a scene from a new point of view, while VP aims to see a scene from a new point of time. These two tasks provide complementary signals to obtain a scene representation, as viewpoint changes from spatial observations inform depth, and temporal observations inform the motion of cameras and individual objects. Inspired by these observations, we propose to study the problem of Video Extrapolation in Space and Time (VEST). We propose a model that leverages the self-supervision and the complementary cues from both tasks, while existing methods can only solve one of them. Experiments show that our method achieves performance better than or comparable to several state-of-the-art NVS and VP methods on indoor and outdoor real-world datasets.

Currently, most social robots interact with their surroundings and humans through sensors that are integral parts of the robots, which limits the usability of the sensors, human-robot interaction, and interchangeability. A wearable sensor garment that fits many robots is needed in many applications. This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots. The vest consists of touch, temperature, gesture, distance, vision sensors, and a wireless communication module. The IoT feature allows the robot to interact with humans locally and over the Internet. The designed architecture works for any social robot that has a general-purpose graphics processing unit (GPGPU), I2C/SPI buses, Internet connection, and the Robotics Operating System (ROS). The modular design of this architecture enables developers to easily add/remove/update complex behaviors. The proposed software architecture provides IoT technology, GPGPU nodes, I2C and SPI bus mangers, audio-visual interaction nodes (speech to text, text to speech, and image understanding), and isolation between behavior nodes and other nodes. The proposed IoT solution consists of related nodes in the robot, a RESTful web service, and user interfaces. We used the HTTP protocol as a means of two-way communication with the social robot over the Internet. Developers can easily edit or add nodes in C, C++, and Python programming languages. Our architecture can be used for designing more sophisticated behaviors for social humanoid robots.

In recent years there have been exciting breakthroughs in wearable technologies, like smartwatches that can monitor your breathing and blood oxygen levels. But what about a wearable that can detect how you move as you do a physical activity or play a sport, and could potentially even offer feedback on how to improve your technique? And, as a major bonus, what if the wearable were something you'd actually already be wearing, like a shirt of a pair of socks? That's the idea behind a new set of MIT-designed clothing that use special fibers to sense a person's movement via touch. Among other things, the researchers showed that their clothes can actually determine things like if someone is sitting, walking, or doing particular poses.

Despite the Consumer Electronics Show (CES) taking place online for the first time in its 54-year history, the all-digital 2021 CES still served as a window to the near future, featuring more than 1,800 exhibitors showcasing their wares – virtually speaking. Granted, it can be tougher to assess how impressive these products are without seeing them with your own eyes – like the latest TV technologies or self-driving cars – but the show managed to surprise and delight with several innovative offerings. As an annual tradition during CES, the following is a few gadgets I'd actually shell out money for – even if they're not available for a long while. Galaxy S21 or iPhone 12?:How Samsung and Apple smartphones stack up Teased by Samsung earlier in the week, Bot Handy is a domestic robot that can roam around your home to perform chores – such as picking up clothes from the floor or loading the dishwasher – but my favorite feature is pouring a glass of wine and bringing it to you. After all, after the year we've just endured, who wouldn't want this kind of high-tech pampering.

Time series forecasting is a challenging task with applications in a wide range of domains. Auto-regression is one of the most common approaches to address these problems. Accordingly, observations are modelled by multiple regression using their past lags as predictor variables. We investigate the extension of auto-regressive processes using statistics which summarise the recent past dynamics of time series. The result of our research is a novel framework called VEST, designed to perform feature engineering using univariate and numeric time series automatically. The proposed approach works in three main steps. First, recent observations are mapped onto different representations. Second, each representation is summarised by statistical functions. Finally, a filter is applied for feature selection. We discovered that combining the features generated by VEST with auto-regression significantly improves forecasting performance. We provide evidence using 90 time series with high sampling frequency. VEST is publicly available online.

Engineers are making strides on the design of four-legged robots and their ability to run, jump and even do backflips. But getting two-legged, humanoid robots to exert force or push against something without falling has been a significant stumbling block. Now engineers at MIT and the University of Illinois at Urbana-Champaign have developed a method to control balance in a two-legged, teleoperated robot -- an essential step toward enabling a humanoid to carry out high-impact tasks in challenging environments. The team's robot, physically resembling a machined torso and two legs, is controlled remotely by a human operator wearing a vest that transmits information about the human's motion and ground reaction forces to the robot. Through the vest, the human operator can both direct the robot's locomotion and feel the robot's motions.