The rapid advancement in physical human-robot interaction (HRI) has accelerated the development of soft robot designs and controllers. Controlling soft robots, especially soft hand grasping, is challenging due to their continuous deformation, motivating the use of reduced model-based controllers for real-time dynamic performance. Most existing models, however, suffer from computational inefficiency and complex parameter identification, limiting their real-time applicability. To address this, we propose a paradigm coupling Pseudo-Rigid Body Modeling with the Logarithmic Decrement Method for parameter estimation (PRBM plus LDM). Using a soft robotic hand test bed, we validate PRBM plus LDM for predicting position and force output from pressure input and benchmark its performance. We then implement PRBM plus LDM as the basis for closed-loop position and force controllers. Compared to a simple PID controller, the PRBM plus LDM position controller achieves lower error (average maximum error across all fingers: 4.37 degrees versus 20.38 degrees). For force control, PRBM plus LDM outperforms constant pressure grasping in pinching tasks on delicate objects: potato chip 86 versus 82.5, screwdriver 74.42 versus 70, brass coin 64.75 versus 35. These results demonstrate PRBM plus LDM as a computationally efficient and accurate modeling technique for soft actuators, enabling stable and flexible grasping with precise force regulation.

Underactuated soft robot hands offer inherent safety and adaptability advantages over rigid systems, but developing dexterous manipulation skills remains challenging. While imitation learning shows promise for complex manipulation tasks, traditional approaches struggle with soft systems due to demonstration collection challenges and ineffective state representations. We present KineSoft, a framework enabling direct kinesthetic teaching of soft robotic hands by leveraging their natural compliance as a skill teaching advantage rather than only as a control challenge. KineSoft makes two key contributions: (1) an internal strain sensing array providing occlusion-free proprioceptive shape estimation, and (2) a shape-based imitation learning framework that uses proprioceptive feedback with a low-level shape-conditioned controller to ground diffusion-based policies. This enables human demonstrators to physically guide the robot while the system learns to associate proprioceptive patterns with successful manipulation strategies. We validate KineSoft through physical experiments, demonstrating superior shape estimation accuracy compared to baseline methods, precise shape-trajectory tracking, and higher task success rates compared to baseline imitation learning approaches.

-- Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly di fficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers. I ntroduction Robots are being used to automate tasks previously performed by humans, with robot hands playing a particularly important role. In a social implementation, changing hands according to the task is problematic in terms of implementation cost. However, a robot hand that can perform many tasks with a single hand has advantages such as greatly reducing the cost of introduction and contributing greatly to the realization of an automated society. Most tools in society are made to fit human hands, so the human mimetic robot hand can be implemented in society without the use of special tools.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. If you've ever wondered why Cassie continually takes steps like she's walking across something hot, this video will make sense to you: I hope that cost isn't all in the feet, because those might be slightly more compliant than they were before. If you need some hot foot relief, here's a video of MARLO walking uphill in the snow, just like I used to on my way to and from school.

Researchers at Cornell University have developed a soft robotic hand with a touch delicate enough to sort tomatoes and find the ripest one. Researchers at Cornell University have developed a soft robotic hand with a touch delicate enough to sort tomatoes and find the ripest one. Robotics researchers at Cornell University made a hand that has something close to a human touch -- it can not only touch delicate items but also sense the shape and texture of what it comes into contact with. Such a soft robot hand is a step forward for the growing field of soft robotics -- the kind of technology that's already used in warehouses to handle food or other products. But it also holds promise for better prosthetics, robots to interact directly with people or with fragile objects, or robots to squeeze into tight spaces.