With a slender redundant body, the tendon-driven robot (TDR) has a large workspace and great maneuverability while working in complex environments. TDR comprises multiple independently controlled robot segments, each with a set of driving tendons. While increasing the number of robot segments enhances dexterity and expands the workspace, this structural expansion also introduces intensified inter-segmental coupling. Therefore, achieving precise TDR control requires more complex models and additional motors. This paper presents a reconfigurable tendon-driven robot (RTR) equipped with innovative lockable joints. Each joint's state (locked/free) can be individually controlled through a pair of antagonistic tendons, and its structure eliminates the need for a continuous power supply to maintain the state. Operators can selectively actuate the targeted robot segments, and this scheme fundamentally eliminates the inter-segmental coupling, thereby avoiding the requirement for complex coordinated control between segments. The workspace of RTR has been simulated and compared with traditional TDRs' workspace, and RTR's advantages are further revealed. The kinematics and statics models of the RTR have been derived and validation experiments have been conducted. Demonstrations have been performed using a seven-joint RTR prototype to show its reconfigurability and moving ability in complex environments with an actuator pack comprising only six motors.

Abstract-- While its biological significance is welldocumented, its application in soft robotics, particularly for the transport of fragile and irregularly shaped objects, remains underexplored. This study presents a modular soft robotic actuator system that addresses these challenges through a scalable, adaptable, and repairable framework, offering a cost-effective solution for versatile applications. Experimental results validate the system's ability to accommodate objects with varying geometries and material characteristics, balancing robustness with flexibility. Peristalsis, defined as the involuntary, wave-like contraction and relaxation of circular and longitudinal muscles [1], is a widespread biological mechanism essential for various functions in animals and humans. Figure 1: Overview of the actuator's capability to grasp delicate The process provides slow but stable [3] coils, electroactive polymers, artificial muscles), and control and adaptable transportation [8], minimizing energy consumption strategies (e.g., thermal feedback, pressure feedback, potentiometer and enabling movement through small or irregular feedback).

Robotic surface consisting of many actuators can change shape to perform tasks, such as facilitating human-machine interactions and transporting objects. Increasing the number of actuators can enhance the robot's capacity, but controlling them requires communication bandwidth to increase equally in order to avoid time delays. We propose a novel control method that has constant time delays no matter how many actuators are in the robot. Having a distributed nature, the method first approximates target shapes, then broadcasts the approximation coefficients to the actuators, and relies on themselves to compute the inputs. We build a robotic pin array and measure the time delay as a function of the number of actuators to confirm the system size-independent scaling behavior. The shape-changing ability is achieved based on function approximation algorithms, i.e. discrete cosine transform or matching pursuit. We perform experiments to approximate target shapes and make quantitative comparison with those obtained from standard sequential control method. A good agreement between the experiments and theoretical predictions is achieved, and our method is more efficient in the sense that it requires less control messages to generate shapes with the same accuracy. Our method is also capable of dynamic tasks such as object manipulation.

Foundation models and large language models have shown immense human-like understanding and capabilities for generating text and digital media. However, foundation models that can freely sense, interact, and actuate the physical world like in the digital domain is far from being realized. This is due to a number of challenges including: 1) being constrained to the types of static devices and sensors deployed, 2) events often being localized to one part of a large space, and 3) requiring dense and deployments of devices Figure 1: RASP autonomous payload reconfiguration to achieve full coverage. As a critical step towards enabling to execute user specified task foundation models to successfully and freely interact with the physical environment, we propose RASP, a modular and Scaling up, there are few works that explore the use reconfigurable sensing and actuation platform that allows of LLMs to actuate our environments, particularly smart drones to autonomously swap onboard sensors and actuators homes [16, 17, 32], where events and actions may occur anywhere in only 25 seconds, allowing a single drone to quickly adapt in the space. These works generally focus on adapting to a diverse range of tasks. We demonstrate through real LLMs as a human-like interface to actuate common internetconnected smart home deployments that RASP enables FMs and LLMs smart appliances (e.g., speakers, television, air to complete diverse tasks up to 85% more successfully by conditioning, etc.). Much like how FMs enable general human allowing them to target specific areas with specific sensors language and sensory understanding and responses, and actuators on-the-fly.

Experiments on physical continuum robot are the gold standard for evaluations. Currently, as no commercial continuum robot platform is available, a large variety of early-stage prototypes exists. These prototypes are developed by individual research groups and are often used for a single publication. Thus, a significant amount of time is devoted to creating proprietary hardware and software hindering the development of a common platform, and shifting away scarce time and efforts from the main research challenges. We address this problem by proposing an open-source actuation module, which can be used to build different types of continuum robots. It consists of a high-torque brushless electric motor, a high resolution optical encoder, and a low-gear-ratio transmission. For this letter, we create three different types of continuum robots. In addition, we illustrate, for the first time, that continuum robots built with our actuation module can proprioceptively detect external forces. Consequently, our approach opens untapped and under-investigated research directions related to the dynamics and advanced control of continuum robots, where sensing the generalized flow and effort is mandatory. Besides that, we democratize continuum robots research by providing open-source software and hardware with our initiative called the Open Continuum Robotics Project, to increase the accessibility and reproducibility of advanced methods.