CAFEs (Collaborative Agricultural Floating Endeffectors) is a new robot design and control approach to automating large-scale agricultural tasks. Based upon a cable driven robot architecture, by sharing the same roller-driven cable set with modular robotic arms, a fast-switching clamping mechanism allows each$CAFE$to clamp onto or release from the moving cables, enabling both independent and synchronized movement across the workspace. The methods developed to enable this system include the mechanical design, precise position control and a dynamic model for the spring-mass liked system, ensuring accurate and stable movement of the robotic arms. The system's scalability is further explored by studying the tension and sag in the cables to maintain performance as more robotic arms are deployed. Experimental and simulation results demonstrate the system's effectiveness in tasks including pick-and-place showing its potential to contribute to agricultural automation.

This study presents an innovative offset-trimmed helicoids (OTH) structure, featuring a tunable deformation center that emulates the flexibility of human fingers. This design significantly reduces the actuation force needed for larger elastic deformations, particularly when dealing with harder materials like thermoplastic polyurethane (TPU). The incorporation of two helically routed tendons within the finger enables both in- plane bending and lateral out-of-plane transitions, effectively expanding its workspace and allowing for variable curvature along its length. Compliance analysis indicates that the compliance at the fingertip can be fine-tuned by adjusting the mounting placement of the fingers. This customization enhances the gripper's adaptability to a diverse range of objects. By leveraging TPU's substantial elastic energy storage capacity, the gripper is capable of dynamically rotating objects at high speeds, achieving approximately 60° in just 15 milliseconds. The three-finger gripper, with its high dexterity across six degrees of freedom, has demonstrated the capability to successfully perform intricate tasks. One such example is the adept spinning of a rod within the gripper's grasp.

To exploit the compliant capabilities of soft robot arms we require controller which can exploit their physical capabilities. Teleoperation, leveraging a human in the loop, is a key step towards achieving more complex control strategies. Whilst teleoperation is widely used for rigid robots, for soft robots we require teleoperation methods where the configuration of the whole body is considered. We propose a method of using an identical ‘physical twin’, or demonstrator of the robot. This tendon robot can be back-driven, with the tendon lengths providing configuration perception, and enabling a direct map-ping of tendon lengths for the execture. We demonstrate how this teleoperation across the entire configuration of the robot enables complex interactions with exploit the envrionment, such as squeezing into gaps. We also show how this method can generalize to robots which are a larger scale that the physical twin, and how, tuneability of the stiffness properties of the physical twin simplify its use.

Teleoperated robotic manipulators enable the collection of demonstration data, which can be used to train control policies through imitation learning. However, such methods can require significant amounts of training data to develop robust policies or adapt them to new and unseen tasks. While expert feedback can significantly enhance policy performance, providing continuous feedback can be cognitively demanding and time-consuming for experts. To address this challenge, we propose using a cable-driven teleoperation system that can provide spatial corrections with 6 degrees of freedom to the trajectories generated by a policy model. Specifically, we propose a correction method termed Decaying Relative Correction (DRC), which is based upon the spatial offset vector provided by the expert and exists temporarily, reducing the number of intervention steps required by an expert. Our results demonstrate that DRC reduces the required expert intervention rate by 30% compared to a standard absolute corrective method. Furthermore, we show that integrating DRC within an online imitation learning framework rapidly increases the success rate of manipulation tasks such as raspberry harvesting and cloth wiping.

This article proposes a novel method to determine the optimal cable attachment configuration for reconfigurable cable-driven parallel robots (RCDPRs) considering different workspace conditions. It is shown that wrench-feasible, wrench-closure, and interference-free conditions can be formulated into inequality constraints by considering the cable attachment points as polynomial functions or variables. Furthermore, the proposed method determines the optimal cable attachment location that minimizes the cable force or maximizes the tension factor kinematically at each pose. The proposed formulation can be resolved by different optimization techniques, such as semidefinite programming relaxation and multivariable gradient-based optimization solvers. The proposed approach can be implemented on RCDPRs from low to high degrees-of-freedom and a wide range of obstacles. The proposed formulation can be widely applied for different reconfiguration mechanisms, such as rails, UGV, and UAV.