Linguistic steganography enables covert communication through embedding secret messages into innocuous texts; however, current methods face critical limitations in payload capacity and security. Traditional modification-based methods introduce detectable anomalies, while retrieval-based strategies suffer from low embedding capacity. Modern generative steganography leverages language models to generate natural stego text but struggles with limited entropy in token predictions, further constraining capacity. To address these issues, we propose an entropy-driven framework called RTMStega that integrates rank-based adaptive coding and context-aware decompression with normalized entropy. By mapping secret messages to token probability ranks and dynamically adjusting sampling via context-aware entropy-based adjustments, RTMStega achieves a balance between payload capacity and imperceptibility. Experiments across diverse datasets and models demonstrate that RTMStega triples the payload capacity of mainstream generative steganography, reduces processing time by over 50%, and maintains high text quality, offering a trustworthy solution for secure and efficient covert communication.

--This paper presents Forte, a fully 3D-printable, 6-DoF robotic arm designed to achieve near industrial-grade performance - 0 . As an accessible robot for broad applications across classroom education to AI experiments, Forte pushes forward the performance limitations of existing low-cost educational arms. We introduce a cost-effective mechanical design that combines capstan-based cable drives, timing belts, simple tensioning mechanisms, and lightweight 3D-printed structures, along with topology optimization for structural stiffness. Through careful drivetrain engineering, we minimize backlash and maintain control fidelity without relying on high-power electronics or expensive manufacturing processes. Experimental validation demonstrates that Forte achieves high repeatability and load capacity, offering a compelling robotic platform for both classroom instruction and advanced robotics research. Can we build a 6-degree-of-freedom (DoF) robotic arm with a material cost under $400, while achieving a half-meter workspace, a payload capacity of more than 0.5 kg, and repeatability within 0. 5 mm? We introduce Forte, a fully 3D-printed robotic manipulator, developed to affirmatively answer this question. In light of surging interest in robotics and artificial intelligence, providing accessible, hands-on educational tools has never been more important, as practical experience and experimental validation are essential components of robotics education.

-- Existing multi - finger robotic hands face several limitations, including excessive weight, mechanical complexity, high cost, and constraints in both payload capacity and de - grees of freedom (DoF). These challenges hinder their wide adoption, especially when paired with collaborative robotic arms with limited payload capacity. To address these challenges, we present Krysalis Hand, a five - finger robotic end - effector that combines a lightweight design, high payload capacity, and a high number of degrees of freedom (DoF) to enable dexterous manipulation in both industrial and research settings. Each finger joint features a self - locking mechanism that allows the hand to sustain large external forces without active motor engagement. This approach shifts the payload limitation from the motor strength to the mechanical strength of the hand, allowing the use of smaller, more cost - effective motors. With 18 DoF and weighing only 790 grams, the Krysalis Hand delivers an active squeezing force of 10 N per finger and supports a passive payload capacity exceeding 10 lbs. These characteristics make Krysalis Hand one of the lightest, strongest, and most dexterous robotic end - effectors of its kind. Experimental evaluations validate its ability to perform intricate manipulation tasks and handle heavy payloads, underscoring its potential for industrial applications as well as academic research. HE rise of automation in recent decades has funda - mentally transformed modern manufacturing, delivering greater efficiency, reduced costs, and increased adaptability [1]. However, due to software complexity, hardware con - straints, and limited adaptability, assembly floors have been the least beneficiaries of automation. The technological lag in assembly automation, partly rooted in Moravec's paradox, stems primarily from the technical complexity of even the simplest tasks, such as threading a wire through a hole or connecting an electrical plug [2], let alone assembling intricate parts. On the software front, with recent advances in machine learning, the assimi - lation of large volumes of multi - modal sensory data and the generation of high - dimensional actions is now more feasible than ever before [5], [6].

Torsion Resistant Strain Limiting Layers Enable High Grip Strength of Electrically-Driven Handed Shearing Auxetic Grippers Ian Good, Srivatsan Balaji, and Jeffrey I. Lipton Abstract --Soft grippers have demonstrated a strong ability to successfully pick and manipulate many objects. A key limitation to their wider adoption is their inability to grasp larger payloads due to objects slipping out of grasps. We have overcome this limitation by introducing a torsionally rigid strain limiting layer (TR-SLL). This reduces out-of-plane bending while maintaining the gripper's softness and in-plane flexibility. We characterize the design space of the strain limiting layer and Handed Shearing Auxetic (HSA) actuators for a soft gripper using simulation and experiment. The inclusion of the TR-SLL with HSAs enables HSA grippers to be made with a single digit. We found that the use of our TR-SLL HSA gripper enabled pinch grasping of payloads over 1 kg. We demonstrate a lifting capacity of 5 kg when loading using the TR-SLL. We also demonstrate a peak pinch grasp force of 5.8 N, and a peak planar caging force of 14.5 N. Finally, we test the TR-SLL gripper on a suite of 43 YCB objects. We show success on 37 objects demonstrating significant capabilities. I NTRODUCTION Soft robotic fingers have focused on emulating the ability of human and other biotas compliance when bending [1]. However, the key to human's remarkable grip is that our fingers can simultaneously bend while resisting torsion and lateral loading. People rely on a rigid skeleton with discrete joints to provide this selective compliance. We build upon a previous conference paper that introduced the torsion resistant strain limiting layer (TR-SLL) [2]. The TR-SLL provides soft grippers with same torsion resistance of a skeleton without discretization. This work extends this to entirely electrically driven grippers. This allows a single Handed Shearing Auxetic (HSA) to be used in gripper and produce a high holding force. The TR-SLLs constrict bending and serves as a reaction body for the HSA.

Pneunets are the primary form of soft robotic grippers. A key limitation to their wider adoption is their inability to grasp larger payloads due to objects slipping out of grasps. We have overcome this limitation by introducing a torsionally rigid strain limiting layer (TRL). This reduces out-of-plane bending while maintaining the gripper's softness and in-plane flexibility. We characterize the design space of the strain limiting layer for a Pneu-net gripper using simulation and experiment and map bending angle and relative grip strength. We found that the use of our TRL reduced out-of-plane bending by up to 97.7% in testing compared to a benchmark Pneu-net gripper from the Soft Robotics Toolkit. We demonstrate a lifting capacity of 5kg when loading using the TRL. We also see a relative improvement in peak grip force of 3N and stiffness of 1200N/m compared to 1N and 150N/m for a Pneu-net gripper without our TRL at equal pressures. Finally, we test the TRL gripper on a suite of six YCB objects above the demonstrated capability of a traditional Pneu-net gripper. We show success on all but one demonstrating significant increased capabilities.

Alphabet-owned Wing has been trying to make drone delivery an actual thing, but the relatively minuscule payload capacity of modern delivery aircraft has been a serious obstacle. The company just unveiled a new drone that's a step in the right direction. The new model can handle payloads of up to five pounds, which is twice as much as Wing's previous flagship drone. It can also travel up to 65 MPH, which is pretty darned fast. The onboard battery allows for a 12 mile round trip, which is in line with previous metrics, so that translates to an under six-minute delivery time. That certainly beats pizza delivery.

A drone called the T-600 - named after the Terminator - successfully launched a torpedo from the sky. BAE Systems demonstrated the feat during a NATO training exercise, which saw a human controller fly the quadcopter strapped with the torpedo from a dock and over the ocean, where it let the weapon drop. The electric-powered, car-sized T-600 has a payload capacity of 441 pounds, tops speeds of 87 miles per hour and has a range of up to 50 miles. The demonstration aimed to showcase the anti-submarine warfare capabilities of the T-600 and its potential for automated logistics, resupply, casualty and evacuation. The T-600 is the AI-powered machine that takes over the world in the iconic Terminator series and features a combat endoskeleton made of titanium alloy, sometimes covered in synthetic latex.

This paper tackles the task assignment and trajectory generation problem for bird diverter installation using a fleet of multi-rotors. The proposed motion planner considers payload capacity, recharging constraints, and utilizes Signal Temporal Logic (STL) specifications for encoding mission objectives and temporal requirements. An event-based replanning strategy is introduced to handle unexpected failures and ensure operational continuity. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation. Simulations in MATLAB and Gazebo, as well as field experiments, demonstrate the effectiveness and validity of the approach in a mock-up scenario.

To enhance network reliability have been developed, including active and and minimize power outages, electricity supply passive designs. Active bird diverters utilize winddriven companies invest significant resources in inspection components, while passive diverters, such as and maintenance operations [1]. Among these activities, helical objects made of plastic or aluminum, are attached the installation of bird diverters on power lines to power cables to serve as visual markers (see (see Figure 1) is essential to mitigate the risk of bird Figure 1). Additionally, alternative techniques, such as collisions [2] and improve their visibility [3]. Bird visual and auditory deterrents, have been developed mortality caused by power line collisions is a significant to mitigate bird collisions. Visual deterrents employ concern, particularly in areas with diverse bird markers or reflective materials to enhance visibility populations or during migratory seasons.

Abstract-- The use of Micro Aerial Vehicles (MAVs) for inspection and surveillance missions has proved to be extremely useful, however, their usability is negatively impacted by the large power requirements and the limited operating time. This work describes the design and development of a novel hybrid aerial-ground vehicle, enabling multi-modal mobility and long operating time, suitable for long-endurance inspection and monitoring applications. The vehicle's performance is evaluated To the best of our knowledge, this is the most complete review of research available for multimodal During the last decade, Micro Aerial Vehicles (MAVs) aerial-ground vehicles. This work aims at addressing have received a great deal of attention, both academically and some of the limitations in prior designs, by exploiting the commercially, due to their ability to quickly reach areas of strengths of both active and passive solutions offered for the interest, overcome obstacles, and provide an elevated view of ground actuation mechanism, while taking into consideration the environment. The applications in which MAVs are used a generous payload required for most industrial applications.