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Researchers in human–robot collaboration have extensively studied methods for inferring human intentions and predicting their actions, as this is an important precursor for robots to provide useful assistance. We review contemporary methods for intention inference and human activity prediction. Our survey finds that intentions and goals are often inferred via Bayesian posterior estimation and Markov decision processes that model internal human states as unobserved variables or represent both agents in a shared probabilistic framework. An alternative approach is to use neural networks and other supervised learning approaches to directly map observable outcomes to intentions and to make predictions about future human activity based on past observations. That said, due to the complexity of human intentions, existing work usually reasons about limited domains, makes unrealistic simplifications about intentions, and is mostly constrained to short-term predictions. This state of the art provides opportunity for future research that could include more nuanced models of intents, reason over longer horizons, and account for the human tendency to adapt. 

Designing plant-driven actuators presents an opportunity to create new types of devices that grow, age, and decay, such as robots that embody these qualities in their physical structure. Plant-robot hybrids that grow and decay incorporate unpredictable and gradual transformations inherent across living organisms and suggest an alternative to the design principles of immediacy, responsiveness, control, accuracy, and durability commonly found in robotic design. To explore this, we present a design space of primitives for plant-driven robotic actuators. Proof-of-concept prototypes illustrate how concepts like slow change, slow movement, decay, and destruction can be incorporated into robotic forms. We describe the design considerations required for building plant-driven actuators for robots, including experimental findings regarding the mechanical properties of plant forces. Finally, we speculate on the potential benefits of plant-robot hybrids to interactive domains such as robotics. 

Biological skin has numerous functions like protection, sensing, expression, and regulation. On the contrary, a robot’s skin is usually regarded as a passive and static separation between the body and environment. In this article, we explore the design opportunities of a robot’s skin as a socially expressive medium. Inspired by living organisms, we discuss the roles of interactive robotic skin from four perspectives: expression, perception, regulation, and mechanical action. We focus on the expressive function of skin to sketch design concepts and present a flexible technical method for embodiment. The proposed method integrates pneumatically actuated dynamic textures on soft skin, with forms and kinematic patterns generating a variety of visual and haptic expressions. We demonstrate the proposed design space with six texture-changing skin prototypes and discuss their expressive capacities. 

Touchibo is a modular robotic platform for enriching interpersonal communication in human-robot group activities, suitable for children with mixed visual abilities. Touchibo incorporates several modalities, including dynamic textures, scent, audio, and light. Two prototypes are demonstrated for supporting storytelling activities and mediating group conversations between children with and without visual impairment. Our goal is to provide an inclusive platform for children to interact with each other, perceive their emotions, and become more aware of how they impact others. 

This article presents the design process of a supernumerary wearable robotic forearm (WRF), along with methods for stabilizing the robot’s end-effector using human motion prediction. The device acts as a lightweight “third arm” for the user, extending their reach during handovers and manipulation in close-range collaborative activities. It was developed iteratively, following a user-centered design process that included an online survey, contextual inquiry, and an in-person usability study. Simulations show that the WRF significantly enhances a wearer’s reachable workspace volume, while remaining within biomechanical ergonomic load limits during typical usage scenarios. While operating the device in such scenarios, the user introduces disturbances in its pose due to their body movements. We present two methods to overcome these disturbances: autoregressive (AR) time series and a recurrent neural network (RNN). These models were used for forecasting the wearer’s body movements to compensate for disturbances, with prediction horizons determined through linear system identification. The models were trained offline on a subset of the KIT Human Motion Database, and tested in five usage scenarios to keep the 3D pose of the WRF’s end-effector static. The addition of the predictive models reduced the end-effector position errors by up to 26% compared to direct feedback control.