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This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of the spacecraft, under actuation. Different spacecraft…
Teleoperation is a cornerstone of embodied-robot learning, and bimanual dexterous teleoperation in particular provides rich demonstrations that are difficult to obtain with fully autonomous systems. While recent studies have proposed…
Most existing safe reinforcement learning (RL) benchmarks focus on robotics and control tasks, offering limited relevance to high-stakes domains that involve structured constraints, mixed-integer decisions, and industrial complexity. This…
Wearable devices like smart glasses have gained popularity across various applications. However, their limited computational capabilities pose challenges for tasks that require extensive processing, such as image and video processing,…
The recent increase in yearly spacecraft launches and the high number of planned launches have raised questions about maintaining accessibility to space for all interested parties. A key to sustaining the future of space-flight is the…
Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been…
The rise of embodied AI has greatly improved the possibility of general mobile agent systems. At present, many evaluation platforms with rich scenes, high visual fidelity and various application scenarios have been developed. In this paper,…
We address load-parameter estimation in cooperative aerial transport with time-varying mass and inertia, as in fluid-carrying payloads. Using an intrinsic manifold model of the multi-quadrotor-load dynamics, we combine a geometric tracking…
Calculating the effort required to complete a task has always been somewhat difficult, as it depends on each person and becomes very subjective. For this reason, different methodologies were developed to try to standardize these procedures.…
Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and…
This paper presents a learning-based approach for accurately estimating the 3D shape of flexible continuum robots subjected to external loads. The proposed method introduces a spatiotemporal neural network architecture that fuses…
Two regimes permitting safe physical human-robot interaction, speed and separation monitoring and safety-rated monitored stop, depend on reliable perception of the space surrounding the robot. This can be accomplished by visual sensors…
Building intelligent autonomous systems at any scale is challenging. The sensing and computation constraints of a microrobot platform make the problems harder. We present improvements to learning-based methods for on-board learning of…
Over a couple of decades, space junk has increased rapidly, which has caused significant threats to the LEO operation satellites. An Active Debris Removal $(ADR)$ concept continuously evolves for space junk removal. One of the ADR methods…
OBJECTIVE: The TER system is a robot-based tele-echography system allowing remote ultrasound examination. The specialist moves a mock-up of the ultrasound probe at the master site, and the robot reproduces the movements of the real probe,…
The out-of-time-ordered correlator (OTOC) is a powerful tool for probing quantum information scrambling, a fundamental process by which local information spreads irreversibly throughout a quantum many-body system. Experimentally measuring…
Robot learning methods have recently made great strides, but generalization and robustness challenges still hinder their widespread deployment. Failing to detect and address potential failures renders state-of-the-art learning systems not…
This paper addresses robotic system engineering for safety- and mission-critical applications by bridging the gap between high-level objectives and formal, executable specifications. The proposed method, Robotic System Task to Model…
Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically…
Learning has propelled the cutting edge of performance in robotic control to new heights, allowing robots to operate with high performance in conditions that were previously unimaginable. The majority of the work, however, assumes that the…