Humans and mammals possess their own feet. Using the mobility of their feet, they are able to walk in various environments such as plain land, desert, swamp, and so on. Previously developed biped robots and four-legged robots did not employ such adaptable foot. In this work, a biomimetic foot mechanism is investigated through analysis of the foot structure of the human-being. This foot mechanism consists of a toe, an ankle, a heel, and springs replacing the foot muscles and tendons. Using five toes and springs, this foot can adapt to various environments. A mathematical modeling for this foot mechanism was performed and its characteristics were observed through numerical simulation.

Stackable mechanism architecture has demonstrated effective gravity-balancing over entire workspaces. Adjustable balancing is required when balancing is broken due to changing the payload at the distal end of a mechanism. In this paper, adjustable balancing of the stackable mechanism for a variable payload is investigated. For this, balancing conditions for three adjustable balancing methods are suggested, and a new balancing method combining a spring and counterweight is considered as an effective means of adjustable balancing for variable payloads. The excellent performance of the system is proven through experiments. Electromyography (EMG) sensors are employed to measure the amount of energy expenditure during the drilling task. It was verified through several tests that an operator holding a drill mounted at the distal end of a stackable arm felt less energy compared to an operator holding the drill directly in free space. The developed balancing arm was successfully applied during a mastoidectomy. A 3-step warning algorithm along with a braking function was found to be effective for safe surgery.

The development of a reliable pick-and-place system for industrial robotics is facing an urgent demand because many manual-labor works, such as piece-picking in warehouses and fulfillment centers tend toward automation. This paper presents an integrated gripper that combines a linkage-driven underactuated gripper with a suction gripping system for picking up a variety of objects in different working environments. The underactuated gripper consists of two fingers, and each finger has three degrees of freedom that are obtained by stacking one five-bar mechanism over one double parallelogram. Furthermore, each finger is actuated by two motors, both of which can be installed at the base owing to the special architecture of the proposed robotic finger. A suction cup is used to grasp objects in narrow spaces and cluttered environments. The combination of the suction and traditional linkage-driven grippers allows stable and reliable grasping under different working environments. Finally, practical experiments using a wide range of objects and under different grasping scenarios are performed to demonstrate the grasping capability of the integrated gripper.

Interactive Object Grasping (IOG) is the task of identifying and grasping the desired object via human-robot natural language interaction. Current IOG systems assume that a human user initially specifies the target object's category (e.g., bottle). Inspired by pragmatics, where humans often convey their intentions by relying on context to achieve goals, we introduce a new IOG task, Pragmatic-IOG, and the corresponding dataset, Intention-oriented Multi-modal Dialogue (IM-Dial). In our proposed task scenario, an intention-oriented utterance (e.g., "I am thirsty") is initially given to the robot. The robot should then identify the target object by interacting with a human user. Based on the task setup, we propose a new robotic system that can interpret the user's intention and pick up the target object, Pragmatic Object Grasping (PROGrasp). PROGrasp performs Pragmatic-IOG by incorporating modules for visual grounding, question asking, object grasping, and most importantly, answer interpretation for pragmatic inference. Experimental results show that PROGrasp is effective in offline (i.e., target object discovery) and online (i.e., IOG with a physical robot arm) settings.

No abstract