Deploying robot learning methods to aerial robots in unstructured environments remains both challenging and promising. While recent advances in deep reinforcement learning (DRL) have enabled end-to-end flight control, the field still lacks systematic design guidelines and a unified infrastructure to support reproducible training and real-world deployment. We present a task-oriented framework for end-to-end DRL in quadrotors that integrates design principles for complex task specification and reveals the interdependencies among simulated task definition, training design principles, and physical deployment. Our framework involves software infrastructure, hardware platforms, and open-source firmware to support a full-stack learning infrastructure and workflow. Extensive empirical results demonstrate robust flight and sim-to-real generalization under real-world disturbances. By reducing the entry barrier for deploying learning-based controllers on aerial robots, our work lays a practical foundation for advancing autonomous flight in dynamic and unstructured environments.

Abstract--This paper introduces GEX, an innovative low-cost dexterous manipulation system that combines the GX11 tri-finger anthropomorphic hand (11 DoF) with the EX12 tri-finger exoskeleton glove (12 DoF), forming a closed-loop teleopera-tion framework through kinematic retargeting for high-fidelity control. Both components employ modular 3D-printed finger designs, achieving ultra-low manufacturing costs while maintaining full actuation capabilities. This full-actuation architecture enables precise bidirectional kinematic calculations, substantially enhancing kinematic retargeting fidelity between the exoskeleton and robotic hand. The proposed system bridges the cost-performance gap in dexterous manipulation research, providing an accessible platform for acquiring high-quality demonstration data to advance embodied AI and dexterous robotic skill transfer learning. Hand dexterity is fundamental to human cognition, enabling active manipulation, tool use, and the way we learn from our environment.

Abstract--The introduction of a teleoperated surgical robotic system designed for minimally invasive procedures enables the emulation of two distinct control modes through a dedicated input device of the surgical console: (1) Inside Control Mode, which emulates tool manipulation near the distal end (i.e., as if the surgeon was holding the tip of the instrument inside the patient's body), and (2) Outside Control Mode, which emulates manipulation near the proximal end (i.e., as if the surgeon was holding the tool externally). The overarching aim of this reported research is to study and compare the surgeon's performance utilizing these two control modes of operation along with various scaling factors in a simulated vitreoretinal surgical setting. The console of Intraocular Robotic Interventional Surgical System (IRISS) was utilized but the surgical robot itself and the human eye anatomy was simulated by a virtual environment (VR) projected microscope view of an intraocular setup to a VR headset. Five experienced vitreoretinal surgeons and five subjects with no surgical experience used the system to perform fundamental tool/tissue tasks common to vitreoretinal surgery including: (1) touch and reset; (2) grasp and drop; (3) inject; (4) circular tracking. The results indicate that Inside Control outperforms Outside Control across multiple tasks and performance metrics. Higher scaling factors (20 and 30) generally provided better performance, particularly for reducing trajectory errors and tissue damage. This improvement suggests that larger scaling factors enable more precise control, making them the preferred option for fine manipulation tasks. However, task completion time was not consistently reduced across all conditions, indicating that surgeons may need to balance speed and accuracy/precision based on specific surgical requirements. By optimizing control dynamics and user interface, robotic teleoperation has the potential to reduce complications, enhance surgical dexterity, and expand the accessibility of high-precision procedures to a broader range of practitioners. In Minimally Invasive Surgery (MIS), surgical instruments are introduced into the body through small ports established at the skin surface or, in the case of ophthalmic procedures, through specific ocular tissues such as the sclera, cornea, or conjunctiva. Unlike open surgery, where the surgeon may manipulate the tool from any position along its shaft--including proximally or distally--MIS confines the surgeon's interaction to the proximal end of the tool, which remains external to the patient's body, while the distal end performs the intervention through the fixed port.

Abstract-- This paper investigates a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes. Our approach reasons about a set of hybrid control modes as an integer-based optimization problem where we select what mode to apply, when to switch to another mode, and the duration for which we are in a given control mode. A sample-based variation is derived to efficiently search the integer domain for optimal solutions. We find our formulation yields strong performance guarantees that can be applied to a number of robotics-related tasks. In addition, our approach is able to synthesize complex algorithms and policies to compound behaviors and achieve challenging tasks. Last, we demonstrate the effectiveness of our approach in a real-world robotic examples that requires reactive switching between long-term planning and high-frequency control. I. INTRODUCTION Modern agile robotic systems must dynamically switch between discrete modes--such as making and breaking contacts--to synthesize complex behaviors like locomotion and manipulation.

Human-AI collaboration is typically offered in one of two of user control levels: guidance, where the AI provides suggestions and the human makes the final decision, and delegation, where the AI acts autonomously within user-defined constraints. Systems that integrate both modes, common in robotic surgery or driving assistance, often overlook shifts in user preferences within a task in response to factors like evolving trust, decision complexity, and perceived control. In this work, we investigate how users dynamically switch between higher and lower levels of control during a sequential decision-making task. Using a hand-and-brain chess setup, participants either selected a piece and the AI decided how it moved (brain mode), or the AI selected a piece and the participant decided how it moved (hand mode). We collected over 400 mode-switching decisions from eight participants, along with gaze, emotional state, and subtask difficulty data. Statistical analysis revealed significant differences in gaze patterns and subtask complexity prior to a switch and in the quality of the subsequent move. Based on these results, we engineered behavioral and task-specific features to train a lightweight model that predicted control level switches ($F1 = 0.65$). The model performance suggests that real-time behavioral signals can serve as a complementary input alongside system-driven mode-switching mechanisms currently used. We complement our quantitative results with qualitative factors that influence switching including perceived AI ability, decision complexity, and level of control, identified from post-game interview analysis. The combined behavioral and modeling insights can help inform the design of shared autonomy systems that need dynamic, subtask-level control switches aligned with user intent and evolving task demands.

Whole-body control (WBC) of humanoid robots has witnessed remarkable progress in skill versatility, enabling a wide range of applications such as locomotion, teleoperation, and motion tracking. Despite these achievements, existing WBC frameworks remain largely task-specific, relying heavily on labor-intensive reward engineering and demonstrating limited generalization across tasks and skills. These limitations hinder their response to arbitrary control modes and restrict their deployment in complex, real-world scenarios. To address these challenges, we revisit existing WBC systems and identify a shared objective across diverse tasks: the generation of appropriate behaviors that guide the robot toward desired goal states. Building on this insight, we propose the Behavior Foundation Model (BFM), a generative model pretrained on large-scale behavioral datasets to capture broad, reusable behavioral knowledge for humanoid robots. BFM integrates a masked online distillation framework with a Conditional Variational Autoencoder (CVAE) to model behavioral distributions, thereby enabling flexible operation across diverse control modes and efficient acquisition of novel behaviors without retraining from scratch. Extensive experiments in both simulation and on a physical humanoid platform demonstrate that BFM generalizes robustly across diverse WBC tasks while rapidly adapting to new behaviors. These results establish BFM as a promising step toward a foundation model for general-purpose humanoid control.

--The high cost of robotic platforms limits students' ability to gain practical skills directly applicable in real-world scenarios. T o address this challenge, this paper presents T ARA, a low-cost, 3D-printed robotic arm designed for accessible robotics education. T ARA includes an open-source repository with design files, assembly instructions, and baseline code, enabling users to build and customize the platform. Experimental validation confirmed accurate performance in basic manipulation tasks. Rather than focusing on performance benchmarking, this work prioritizes educational reproducibility, providing a platform that students and educators can reliably replicate and extend. Robotics is playing an increasingly vital role in both industry and education.

Omnidirectional aerial robots offer full 6-DoF independent control over position and orientation, making them popular for aerial manipulation. Although advancements in robotic autonomy, human operation remains essential in complex aerial environments. Existing teleoperation approaches for multirotors fail to fully leverage the additional DoFs provided by omnidirectional rotation. Additionally, the dexterity of human fingers should be exploited for more engaged interaction. In this work, we propose an aerial teleoperation system that brings the rotational flexibility of human hands into the unbounded aerial workspace. Our system includes two motion-tracking marker sets--one on the shoulder and one on the hand--along with a data glove to capture hand gestures. Using these inputs, we design four interaction modes for different tasks, including Spherical Mode and Cartesian Mode for long-range moving, Operation Mode for precise manipulation, as well as Locking Mode for temporary pauses, where the hand gestures are utilized for seamless mode switching. We evaluate our system on a vertically mounted valve-turning task in the real world, demonstrating how each mode contributes to effective aerial manipulation. This interaction framework bridges human dexterity with aerial robotics, paving the way for enhanced aerial teleoperation in unstructured environments.

As the global population of people with disabilities (PWD) continues to grow, so will the need for mobility solutions that promote independent living and social integration. Wheelchairs are vital for the mobility of PWD in both indoor and outdoor environments. The current SOTA in powered wheelchairs is based on either manually controlled or fully autonomous modes of operation, offering limited flexibility and often proving difficult to navigate in spatially constrained environments. Moreover, research on robotic wheelchairs has focused predominantly on complete autonomy or improved manual control; approaches that can compromise efficiency and user trust. To overcome these challenges, this paper introduces the CoNav Chair, a smart wheelchair based on the Robot Operating System (ROS) and featuring shared control navigation and obstacle avoidance capabilities that are intended to enhance navigational efficiency, safety, and ease of use for the user. The paper outlines the CoNav Chair's design and presents a preliminary usability evaluation comparing three distinct navigation modes, namely, manual, shared, and fully autonomous, conducted with 21 healthy, unimpaired participants traversing an indoor building environment. Study findings indicated that the shared control navigation framework had significantly fewer collisions and performed comparably, if not superior to the autonomous and manual modes, on task completion time, trajectory length, and smoothness; and was perceived as being safer and more efficient based on user reported subjective assessments of usability. Overall, the CoNav system demonstrated acceptable safety and performance, laying the foundation for subsequent usability testing with end users, namely, PWDs who rely on a powered wheelchair for mobility.

Background: Lower limb exoskeletons can enhance quality of life, but widespread adoption is limited by the lack of frameworks to assess their biomechanical and human-robot interaction effects, which are essential for developing adaptive and personalized control strategies. Understanding impacts on kinematics, muscle activity, and HRI dynamics is key to achieve improved usability of wearable robots. Objectives: We propose a systematic methodology evaluate an ankle exoskeleton's effects on human movement during walking and load-carrying (10 kg front pack), focusing on joint kinematics, muscle activity, and HRI torque signals. Materials and Methods: Using Xsens MVN (inertial motion capture), Delsys EMG, and a unilateral exoskeleton, three experiments were conducted: (1) isolated dorsiflexion/plantarflexion; (2) gait analysis (two subjects, passive/active modes); and (3) load-carrying under assistance. Results and Conclusions: The first experiment confirmed that the HRI sensor captured both voluntary and involuntary torques, providing directional torque insights. The second experiment showed that the device slightly restricted ankle range of motion (RoM) but supported normal gait patterns across all assistance modes. The exoskeleton reduced muscle activity, particularly in active mode. HRI torque varied according to gait phases and highlighted reduced synchronization, suggesting a need for improved support. The third experiment revealed that load-carrying increased GM and TA muscle activity, but the device partially mitigated user effort by reducing muscle activity compared to unassisted walking. HRI increased during load-carrying, providing insights into user-device dynamics. These results demonstrate the importance of tailoring exoskeleton evaluation methods to specific devices and users, while offering a framework for future studies on exoskeleton biomechanics and HRI.