This paper presents a novel solution for underwater intention recognition that simultaneously detects head motion and throat vibration, enhancing multimodal human-robot interactions for underwater diving. The system pairs with an underwater supernumerary robotic limb (SuperLimb), providing propulsion assistance to reduce the diver’s physical load and mental fatigue. An inertial measurement unit monitors head motion, while a throat microphone captures vocal vibrations. Learning algorithms process these signals to accurately interpret the diver’s intentions and map them to the SuperLimb for posture management. The system features a compact design optimized for diving scenarios and includes a multimodal, real-time classification algorithm to distinguish various head motions and vocal signals. By collecting and analyzing underwater throat vibration data, the study demonstrates the feasibility of this approach, enabling continuous motion commands for enhanced diving assistance. The results show that the head motion recognition component of the system achieved a high classification accuracy of 95%, and throat vibration classification reached 86% accuracy on land and 89% underwater for various purposes.

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.

We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6$\%$with a higher success rate, and still provides a near-optimal plan.

Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional ground locomotion. The Bennett linkage belongs to a family of overconstrained linkages with three-dimensional spatial motion and unparalleled joint axes. We present the first work regarding the design, modeling, and optimization of the Bennett leg module, enabling lateral locomotion, like the crabs, that was not capable with robotic legs designed with common planar mechanisms. We further explored the concept of overconstrained robots, which is a class of advanced robots based on the design reconfiguration of the Bennett leg modules, serving as a potential direction for future research.

Fast-growing interests in safe and effective robot-environment interactions stimulated global investigations on soft robotics. The inherent compliance of soft robots ensures promising safety features but drastically reduces force capability, thereby complicating system modeling and control. To tackle these limitations, a soft robotic joint with enhanced strength, servo performance, and impact behavior shaping is proposed in this paper, based on novel three-dimensional-printed soft origami rotary actuators. The complete workflow is presented from the concept of origami design and analytical modeling, joint design, fabrication, control, and validation experiments. The proposed approach facilitates a fully customizable joint design towards the desired force capability and motion range. Validation results from models and experiments using multiple fabricated prototypes proved the excellent performance linearity and superior force capability, with 18.5-N·m maximum torque under 180 kPa, and 300-g self-weight. The behavior shaping capability is achieved by a low-level joint-angle servo and a high-level variable-stiffness regulation; this significantly reduces the impact torque by 53% and ensures powerful and safe interactions. The comprehensive guidelines provide insightful references for soft robotic design for wider robotic applications.

Classical rigid-bodied robotic systems are presented with proven success in theoretical development and industrial applications, are recently challenged by the emergence of soft robotics due to a growing need in physical human-robot interactions (pHRI), such as wearable devices, medical robots, personal robots, etc. In this paper, we present the design and fabrication of a robust, hybrid bending actuator build from both rigid and soft components inspired by crustaceans, where its bending radius and axis can be mechanically programmed through the selective activation of the rigid exterior joints, actuated by the soft actuators inside. The hybrid actuator was experimentally measured in terms of bending and force tests to demonstrate the utility of this design. Finally, a case study was presented to demonstrate its capacity to adapt to specific objects geometry, anticipating its potential application in situations where compliance is the priority.

This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation. Inspired by the bending abdomen of lobsters, hybrid actuators are built with serially jointed rigid shells actuated by pressurized soft chambers inside to generate bending motions. Such bio-inspiration absorbs features from the classical rigid-bodied robotics with preciselydefined motion generation, as well as the emerging soft robotics with light-weight, physically safe, and adaptive actuation. The fabrication procedure is described, followed by experiments to mechanically characterize these actuators. Finally, an open-palm glove design integrated with these hybrid actuators are presented for a qualitative case study. A hand rehabilitation system is developed by learning patterns of the sEMG signals from the users forearm to train the assistive glove for hand rehabilitation exercises.