Soft robotic hands compensate for uncertainty in perception and actuation by leveraging passive deformation in their intrinsically compliant hardware, facilitating robust and dexterous interactions with their environment. The ability to adjust the level of compliance during operation has the potential to further improve the performance of these hands by enabling novel interaction strategies. However, achieving variable stiffness mechanically typically requires significant engineering complexity, making these systems difficult to manufacture, prone to error, and expensive. We present a novel, very simple mechanism for achieving variable stiffness. This mechanism employs tendon-driven antagonistic actuation, with Bowden cables connecting elastic elements to servomotors. It supports compact actuator designs, while the Bowden cables facilitate flexible component placement within a robotic system. Following our approach, variable stiffness actuators can be easily manufactured at low-cost from readily available materials. Despite its simplicity, we demonstrate that our mechanism provides consistent and precise control over stiffness levels and contact torques, showcasing its potential for a broad range of applications in soft robotic systems.

Soft robots exhibit complex behaviors despite simple control, due to their inherently compliant hardware which passively deforms upon contact ± a concept commonly referred to as morphological computation. To fully determine the behavior of soft robots, not only their control software but also their passive behavior needs to be programmed. We show that deliberate programming of passive deformation in soft fingertips can significantly influence the grasping and manipulation performance of various robotic grippers. For this, the fingertips display strategically modulated compliance levels across their palmar surface, realized through adjustments to the local thickness of a lattice structure within their soft material, resulting in desired passive deformation. The grippers are operated by human participants, solving diverse tasks involving a variety of objects. We analyze 2025 human trials and show that the distinct passive behaviors programmed into the fingertips significantly affect grasping and manipulation performance. Furthermore, we discovered that specific compliance profiles consistently demonstrate superior performance, indicating that not merely inherent softness by itself, but a purposeful combination of varying compliance levels plays a pivotal role in successful soft interaction.

Soft robotics is an emerging field that yields promising results for tasks that require safe and robust interactions with the environment or with humans, such as grasping, manipulation, and human-robot interaction. Soft robots rely on intrinsically compliant components and are difficult to equip with traditional, rigid sensors which would interfere with their compliance. We propose a highly flexible tactile sensor that is low-cost and easy to manufacture while measuring contact pressures independently from 14 taxels. The sensor is built from piezoresistive fabric for highly sensitive, continuous responses and from a custom-designed flexible printed circuit board which provides a high taxel density. From these taxels, location and intensity of contact with the sensor can be inferred. In this paper, we explain the design and manufacturing of the proposed sensor, characterize its input-output relation, evaluate its effects on compliance when equipped to the silicone-based pneumatic actuators of the soft robotic RBO Hand 2, and demonstrate that the sensor provides rich and useful feedback for learning-based in-hand object recognition.

In this article, we present theRBO Hand 3, a highly capable and versatile anthropomorphic soft hand based on pneumatic actuation. TheRBO Hand 3is designed to enable dexterous manipulation, to facilitate transfer of insights about human dexterity, and to serve as a robust research platform for extensive real-world experiments. It achieves these design goals by combining many degrees of actuation with intrinsic compliance, replicating relevant functioning of the human hand, and by combining robust components in a modular design. TheRBO Hand 3possesses 16 independent degrees of actuation, implemented in a dexterous opposable thumb, two-chambered fingers, an actuated palm, and the ability to spread the fingers. In this article, we derive the design objectives that are based on experimentation with the hand’s predecessors, observations about human grasping, and insights about principles of dexterity. We explain in detail how the design features of theRBO Hand 3achieve these goals and evaluate the hand by demonstrating its ability to achieve the highest possible score in the Kapandji test for thumb opposition, to realize all 33 grasp types of the comprehensive GRASP taxonomy, to replicate common human grasping strategies, and to perform dexterous in-hand manipulation.

Observations of human grasping reveal that the exploitation of environmental constraints is a key structural aspect for the robustness and versatility of human grasping behavior. We analyze 3,400 human grasping trials with 17 subjects grasping 25 objects to show that viewing environmental constraints as the central structural aspect of human grasping yields surprisingly simple representations of human grasping behavior. We present hypothesis-driven experiments that emphasize the centrality of environmental constraints in human grasping and extract from data a simple “grasping plan” that is a generative model for all of the human grasping trials we observed. This grasping plan can in principle be transferred to a robot system in an attempt to leverage environmental constraints to improve the performance of robotic grasping.

As a first step towards transferring human grasping capabilities to robots, we analyzed the grasping behavior of human subjects. We derived a taxonomy in order to adequately represent the observed strategies. During the analysis of the recorded data, this classification scheme helped us to obtain a better understanding of human grasping behavior. We will provide support for our hypothesis that humans exploit compliant contact between the hand and the environment to compensate for uncertainty. We will also show a realization of the resulting grasping strategies on a real robot. It is our belief that the detailed analysis of human grasping behavior will ultimately lead to significant increases in robot manipulation and dexterity.