The ability to accomplish a sit-to-stand (STS) motion is key to increase functional mobility and reduce rehospitalization risks. While raising aid (transfer) devices and partial bodyweight support (rehabilitation) devices exist, both are unable to adjust the STS training to different mobility levels. Therefore, We have developed an STS training device that allows various configurations of impedance and vertical/forward forces to adapt to many training needs while maintaining commercial raising aid transfer capabilities. Experiments with healthy adults (both men and women) of various heights and weights show that the device 1) has a low impact on the natural STS kinematics, 2) can provide precise weight unloading at the patient's center of mass and 3) can add a forward virtual spring to assist the transfer of the bodyweight to the feet for seat-off, at the start of the STS motion. Keywords: Rehabilitation robotics, Force control, Human-robot interaction, Patient transfer, Floor-lift1. INTRODUCTION For patients in movement rehabilitation, accomplishing functional tasks is key to increasing quality of life and reducing the risk of rehospitalization [1, 2]. Training sit-to-stands (STS) is particularly useful as it has a significant correlation with increasing patient muscle power and the balance required to perform standing and walking tasks [3, 4]. Frequent training is essential to prevent muscle atrophy. However, studies indicate that up to 65% of patients hospitalized in short-term care for seven days or longer develop muscle weakness due to prolonged immobility [5]. This is partly due to the current shortage of qualified clinical staff in hospital settings [6]. Clinical staff can use passive lifts to assist the patient's STS motion, such as the Guldmann GPT1 or the ARJO Sara Stedy, which hold the patient's and knees in a fixed position to reduce fall risks. However, using passive lifts for STS training can be exhaustive and lead to injuries for the clinical staff since it relies on them to move the patient's center of mass (CoM) [7, 8].

Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language representations. Among existing approaches, a majority of current VLA systems employ parallel two finger grippers as their default end effectors. However, such grippers face inherent limitations in handling certain real world tasks such as wiping glass surfaces or opening drawers without handles due to insufficient contact area or lack of adhesion. To overcome these challenges, we present a low cost, integrated hardware design that combines a mechanical two finger gripper with a vacuum suction unit, enabling dual mode manipulation within a single end effector. Our system supports flexible switching or synergistic use of both modalities, expanding the range of feasible tasks. We validate the efficiency and practicality of our design within two state of the art VLA frameworks: DexVLA and Pi0. Experimental results demonstrate that with the proposed hybrid end effector, robots can successfully perform multiple complex tasks that are infeasible for conventional two finger grippers alone. All hardware designs and controlling systems will be released.

This paper presents an integrated robotic fused deposition modeling additive manufacturing system featuring closed-loop thermal control and intelligent in-situ defect correction using a 6-degree of freedom robotic arm and an Oak-D camera. The robot arm end effector was modified to mount an E3D hotend thermally regulated by an IoT microcontroller, enabling precise temperature control through real-time feedback. Filament extrusion system was synchronized with robotic motion, coordinated via ROS2, ensuring consistent deposition along complex trajectories. A vision system based on OpenCV detects layer-wise defects position, commanding autonomous re-extrusion at identified sites. Experimental validation demonstrated successful defect mitigation in printing operations. The integrated system effectively addresses challenges real-time quality assurance. Inverse kinematics were used for motion planning, while homography transformations corrected camera perspectives for accurate defect localization. The intelligent system successfully mitigated surface anomalies without interrupting the print process. By combining real-time thermal regulation, motion control, and intelligent defect detection & correction, this architecture establishes a scalable and adaptive robotic additive manufacturing framework suitable for aerospace, biomedical, and industrial applications.

Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a bimanual BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted with intracortical BCI electrodes in the posterior parietal cortex (PPC) and the hand knob region of the motor cortex (MC), reacts at least as fast and precisely as motor intact participants, and drives a simulated vehicle as proficiently as the same control group. This BCI participant, living in California, could also remotely drive a Ford Mustang Mach-E vehicle in Michigan. Our first teledriving task relied on cursor control for speed and steering in a closed urban test facility. However, the final BCI system added click control for full-stop braking and thus enabled bimanual cursor-and-click control for both simulated driving through a virtual town with traffic and teledriving through an obstacle course without traffic in the real world. We also demonstrate the safety and feasibility of BCI-controlled driving. This first-of-its-kind implantable BCI application not only highlights the versatility and innovative potentials of BCIs but also illuminates the promising future for the development of life-changing solutions to restore independence to those who suffer catastrophic neurological injury.

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

Manual tissue extraction from potato tubers for molecular pathogen detection is highly laborious. This study presents a machine-vision-guided, dual-arm coordinated inline robotic system integrating tuber grasping and tissue sampling mechanisms. Tubers are transported on a conveyor that halts when a YOLOv11-based vision system detects a tuber within the workspace of a one-prismatic-degree-of-freedom (P-DoF) robotic arm. This arm, equipped with a gripping end-effector, secures and positions the tuber for sampling. The second arm, a 3-P-DoF Cartesian manipulator with a biopsy punch-based end-effector, then performs tissue extraction guided by a YOLOv10-based vision system that identifies the sampling sites on the tuber such as eyes or stolon scars. The sampling involves four stages: insertion of the punch into the tuber, punch rotation for tissue detachment, biopsy punch retraction, and deposition of the tissue core onto a collection site. The system achieved an average positional error of 1.84 mm along the tuber surface and a depth deviation of 1.79 mm from a 7.00 mm target. The success rate for core extraction and deposition was 81.5%, with an average sampling cycle of 10.4 seconds. The total cost of the system components was under $1,900, demonstrating the system's potential as a cost-effective alternative to labor-intensive manual tissue sampling. Future work will focus on optimizing for multi-site sampling from a single tuber and validation in commercial settings.

Many recent advances in robotic manipulation have come through imitation learning, yet these rely largely on mimicking a particularly hard-to-acquire form of demonstrations: those collected on the same robot in the same room with the same objects as the trained policy must handle at test time. In contrast, large pre-recorded human video datasets demonstrating manipulation skills in-the-wild already exist, which contain valuable information for robots. Is it possible to distill a repository of useful robotic skill policies out of such data without any additional requirements on robot-specific demonstrations or exploration? We present the first such system ZeroMimic, that generates immediately deployable image goal-conditioned skill policies for several common categories of manipulation tasks (opening, closing, pouring, pick&place, cutting, and stirring) each capable of acting upon diverse objects and across diverse unseen task setups. ZeroMimic is carefully designed to exploit recent advances in semantic and geometric visual understanding of human videos, together with modern grasp affordance detectors and imitation policy classes. After training ZeroMimic on the popular EpicKitchens dataset of ego-centric human videos, we evaluate its out-of-the-box performance in varied real-world and simulated kitchen settings with two different robot embodiments, demonstrating its impressive abilities to handle these varied tasks. To enable plug-and-play reuse of ZeroMimic policies on other task setups and robots, we release software and policy checkpoints of our skill policies.

This study addresses a task designed to predict the future success or failure of open-vocabulary object manipulation. In this task, the model is required to make predictions based on natural language instructions, egocentric view images before manipulation, and the given end-effector trajectories. Conventional methods typically perform success prediction only after the manipulation is executed, limiting their efficiency in executing the entire task sequence. We propose a novel approach that enables the prediction of success or failure by aligning the given trajectories and images with natural language instructions. We introduce Trajectory Encoder to apply learnable weighting to the input trajectories, allowing the model to consider temporal dynamics and interactions between objects and the end effector, improving the model's ability to predict manipulation outcomes accurately. We constructed a dataset based on the RT-1 dataset, a large-scale benchmark for open-vocabulary object manipulation tasks, to evaluate our method. The experimental results show that our method achieved a higher prediction accuracy than baseline approaches.

Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen tasks, due to their reliance on behavior cloning exclusively from successful rollouts. Furthermore, they are typically fine-tuned to replicate demonstrations collected by experts under different settings, thus introducing distribution bias and limiting their adaptability to diverse manipulation objectives, such as efficiency, safety, and task completion. To bridge this gap, we introduce GRAPE: Generalizing Robot Policy via Preference Alignment. Specifically, GRAPE aligns VLAs on a trajectory level and implicitly models reward from both successful and failure trials to boost generalizability to diverse tasks. Moreover, GRAPE breaks down complex manipulation tasks to independent stages and automatically guides preference modeling through customized spatiotemporal constraints with keypoints proposed by a large vision-language model. Notably, these constraints are flexible and can be customized to align the model with varying objectives, such as safety, efficiency, or task success. We evaluate GRAPE across a diverse array of tasks in both real-world and simulated environments. Experimental results demonstrate that GRAPE enhances the performance of state-of-the-art VLA models, increasing success rates on in-domain and unseen manipulation tasks by 51.79% and 60.36%, respectively. Additionally, GRAPE can be aligned with various objectives, such as safety and efficiency, reducing collision rates by 44.31% and rollout step-length by 11.15%, respectively. All code, models, and data are available at https://grape-vla.github.io/

This paper introduces an innovative control approach for teleoperating a robot in close proximity to a human operator, which could be useful to control robots embedded on wheelchairs. The method entails establishing a virtual connection between a specific body part and the robot's end-effector, visually displayed through an Augmented Reality (AR) headset. This linkage enables the transformation of body rotations into amplified effector translations, extending the robot's workspace beyond the capabilities of direct one-to-one mapping. Moreover, the linkage can be reconfigured using a joystick, resulting in a hybrid position/velocity control mode using the body/joystick motions respectively. After providing a comprehensive overview of the control methodology, we present the results of an experimental campaign designed to elucidate the advantages and drawbacks of our approach compared to the conventional joystick-based teleoperation method. The body-link control demonstrates slightly faster task completion and is naturally preferred over joystick velocity control, albeit being more physically demanding for tasks with a large range. The hybrid mode, where participants could simultaneously utilize both modes, emerges as a compromise, combining the intuitiveness of the body mode with the extensive task range of the velocity mode. Finally, we provide preliminary observations on potential assistive applications using head motions, especially for operators with limited range of motion in their bodies.