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This article proposes a novel scheme—based on power-shaping control (PSC)—that can endow flexible-joint robots with both tracking, and interactional, capabilities. In virtue of relying upon the PSC method, this approach entails modest modeling requirements restricted to computation of the gravitational torque vector, and motor-side dynamics terms (typically available in manufacturer datasheets). Hence, it distinguishes itself by obviating the need for calculation of computationally cumbersome link-side dynamics elements, such as the Coriolis and link inertia matrices. In contrast to analogous schemes, the highest order term required by the proposed design is the third derivative of the motor position vector; it therefore avoids the usage of link-jerk feedback that can be detrimental to interactional performance. Moreover, the propounded framework enables utilisation of noncollocated feedback for enhanced tracking accuracy, as well as variable impedance control for interactional performance augmentation. The aforesaid features are effectuated without any reliance on coordinate transformations; thus, the original dynamical model's structure remains immutable throughout. Also, the proposed design's complexity is dependent solely on the gravitational torque vector's dimension (i.e., not on the link inertia or Coriolis terms). Experimental results involving a flexible-joint robot, namely the Rethink Robotics Baxter, corroborate the theoretical analyses, in addition to demonstrating that interactional performance improvements can be achieved via the proposed methodology.

This article presents a novel vision-based proprioception approach for a soft robotic finger that can estimate and reconstruct tactile interactions in terrestrial and aquatic environments. The key to this system lies in the finger's unique metamaterial structure, which facilitates omnidirectional passive adaptation during grasping, protecting delicate objects across diverse scenarios. A compact in-finger camera captures high-framerate images of the finger's deformation during contact, extracting crucial tactile data in real time. We present a volumetric discretized model of the soft finger and use the geometry constraints captured by the camera to find the optimal estimation of the deformed shape. The approach is benchmarked using a motion capture system with sparse markers and a haptic device with dense measurements. Both results show state-of-the-art accuracy, with a median error of 1.96 mm for overall body deformation, corresponding to 2.1$\%$ of the finger's length. More importantly, the state estimation is robust in both on-land and underwater environments as we demonstrate its usage for underwater object shape sensing. This combination of passive adaptation and real-time tactile sensing paves the way for amphibious robotic grasping applications.

Dynamic capture is one of the most challenging issues to be solved in the field of robotics. Although robotic hands/grippers have emerged for decades, there are still few of them that can dynamically capture moving targets because the process is strongly accompanied by impact force and uncertainties in relative gripper/target locations and velocities. In this article, we present the novel design of a multitentacle gripper inspired by the motions of sea anemones. It is found that each tentacle of the sea anemone is not individually able to capture a fish but the collaboration of a larger number of tentacles can greatly enhance the overall capture ability. Based on this concept, 12 continuum arms including active and passive types are proposed and evaluated for their deformation to external forces. In addition, a deployable base, inspired by origami and Sarrus mechanisms, is utilized to change the posture of the continuum arms and drive the active arms. An inertial measurement unit is equipped to sense the impact of the dynamic targets. Finally, a series of experiments proved that the proposed multitentacle gripper could capture dynamic targets with different shapes, velocities, and collision angles, showing satisfactory capture robustness.