Soft robots' ability to safely navigate complex environments motivates the development of algorithms for accurate environmental interaction assessment, enabling greater autonomy. Specifically, strain-based shape and force estimation of continuum robots with embedded soft sensors poses an open challenge mainly owing to continuous softness, anisotropic deformation, and non-linear properties. Mathematical description of deformable soft bodies and accurate estimation of external forces are crucial for achieving controllable and intelligent behaviors of these robots. In this paper, a kinetostatic strain-based modeling for rod-driven soft robots (RDSR) with embedded stretch sensors is proposed, which incorporates local strains, actuation variables, and external interactions. The strain model enables full shape estimation of the robot and prediction of strain variations in soft bodies. Building on this, we develop a force estimator based on predicted and measured sensor and actuator lengths to evaluate 3D external forces, accounting for both orthogonal and tangential components relative to the backbone. Moreover, we introduce a methodology using a novel ellipsoid representation to handle tangential forces that may become insensitive in certain singular configurations. This estimator allows us to either disregard such forces when they do not influence deformation or estimate them when they become observable. Our simulations and experiments demonstrate how this approach can be used to analyze the robot's configuration and successfully estimate external forces. Finally, it is demonstrated that when the continuum arm follows trajectories with higher strain sensitivity, tangential force estimation is significantly improved.

Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increasingly employed to operate in unstructured and unpredictable environments. Soft robots are inherently compliant to safely interact with environments but their high nonlinearity exacerbates control difficulties. Meta-learning provides a powerful tool for fast online model adaptation because it can learn an efficient model from data across different environments. Thus, this work applies the idea of meta-learning for the control of soft robotics. In particular, a target-oriented proactive search strategy is firstly performed to collect environment-specific data efficiently when a new interaction environment occurs. Then meta-learning exploits past experience to train a data-driven probabilistic model prior, and the model prior is online updated to be fast adapted to the new environment. Lastly, a model-based optimal control policy is utilized to drive the robot to desired performance. Our approach controls a soft robotic manipulator to achieve the desired position and contact force simultaneously when interacting with unknown changing environments. Overall, this work provides a viable control approach for soft robots to interact with unknown environments.

This paper presents a soft actuator embedded with conductive liquid metal and shape memory epoxy (SME) which function together to enable self-sensing, tunable mechanical degrees of freedom (DoF), and variable stiffness. We embedded thermoplastic shape memory epoxy in the bottom portion of the actuator. Different sections of the SME could be selectively softened by an implanted conductive silver yarn located at different positions. When an electric current passes through the conductive silver yarn, it induces a phase transition that changes the epoxy from stiff state to compliant state. Each section of SME could be softened within 5 s by applying a current of 200 mA to the silver yarn. To acquire the strain curvature, eGaIn was infused into a microchannel surrounding the chambers of the soft actuator. A spiral-shaped eGaIn sensor was also attached to the tip of the actuator to perceive the contact with reliable dynamic force response. Systematic experiments were performed to characterize the stiffness, tunable DoF, and sensing property. We show the ability of the soft composite actuator to support a weight of 200g at the tip (as a cantilever) while maintaining the shape and the ability to recover its original shape after large bending deformation. In particular, seven different motion patterns could be achieved under the same pneumatic pressure of the actuator due to selectively heating the SME sections. A gripper which was fabricated by assembling two actuators to a base was able to grasp the weight up to 56 times of a single actuator through an appropriate motion pattern. For demonstration purposes, the gripper was used to grasp various objects by adjusting the DoF and stiffness with real-time feedback of the bending strain and the contact force.