However, inmanyapplications including contractual hiringinonlinefreelancing platforms and server allocation in cloud computing systems, the outcome of each action is observed only after a random and action-dependent time.

Abstract--In this letter, we investigate whether classical function allocation--the principle of assigning tasks to either a human or a machine--holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control. Received 7 May 2025; accepted 25 October 2025.

Abstract-- In collaborative human-robot tasks, safety requires not only avoiding collisions but also ensuring safe, intentional physical contact. We present ContactRL, a reinforcement learning (RL) based framework that directly incorporates contact safety into the reward function through force feedback. This enables a robot to learn adaptive motion profiles that minimize human-robot contact forces while maintaining task efficiency. In simulation, ContactRL achieves a low safety violation rate of 0.2% with a high task success rate of 87.7%, outperforming state-of-the-art constrained RL baselines. In order to guarantee deployment safety, we augment the learned policy with a kinetic energy based Control Barrier Function (eCBF) shield. Real-world experiments on an UR3e robotic platform performing small object handovers from a human hand across 360 trials confirm safe contact, with measured normal forces consistently below 10N. These results demonstrate that ContactRL enables safe and efficient physical collaboration, thereby advancing the deployment of collaborative robots in contact-rich tasks.

Abstract-- Object handover is a common form of interaction that is widely present in collaborative tasks. However, achieving it efficiently remains a challenge. We address the problem of ensuring resilient robotic actions that can adapt to complex changes in object pose during human-to-robot object handovers. We propose the use of Virtual Model Control to create an interaction layer that controls the robot and adapts to the dynamic changes in the handover process. Additionally, we propose the use of augmented reality to facilitate bidirectional communication between humans and robots during handovers. We assess the performance of our controller in a set of experiments that demonstrate its resilience to various sources of uncertainties, including complex changes to the object's pose during the handover . Finally, we performed a user study with 16 participants to understand human preferences for different robot control profiles and augmented reality visuals in object handovers. Our results showed a general preference for the proposed approach and revealed insights that can guide further development in adapting the interaction with the user . Human-to-robot object handover is a fundamental task that frequently occurs in collaborative manipulation.

Multi-robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor-intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Pareto-optimal solutions for the conflicting objectives of makespan and transportation cost, but also from the necessity to simultaneously manage payload constraints and finite battery capacity. When robot loads are dynamically updated during planned multi-trip operations, a mandatory recharge triggered by energy constraints introduces an unscheduled load reset. This interaction creates a complex cascading effect that disrupts the entire schedule and renders traditional optimization methods ineffective. To address this challenge, this paper proposes the segment anchoring-based balancing algorithm (SABA). The core of SABA lies in the organic combination of two synergistic mechanisms: the sequential anchoring and balancing mechanism, which leverages charging decisions as `anchors' to systematically reconstruct disrupted routes, while the proportional splitting-based rebalancing mechanism is responsible for the fine-grained balancing and tuning of the final solutions' makespans. Extensive comparative experiments, conducted on a real-world case study and a suite of benchmark instances, demonstrate that SABA comprehensively outperforms 6 state-of-the-art algorithms in terms of both solution convergence and diversity. This research provides a novel theoretical perspective and an effective solution for the multi-robot task allocation problem under energy constraints.

Neural Information Processing Systems http://nips.cc/

The Resource-Constrained Project Scheduling Problem (RCPSP) is a classical scheduling problem that has received significant attention due to of its numerous applications in industry. However, in practice, task durations are subject to uncertainty that must be considered in order to propose resilient scheduling. In this paper, we address the RCPSP variant with uncertain tasks duration (modeled using known probabilities) and aim to minimize the overall expected project duration. Our objective is to produce a baseline schedule that can be reused multiple times in an industrial setting regardless of the actual duration scenario. We leverage Graph Neural Networks in conjunction with Deep Reinforcement Learning (DRL) to develop an effective policy for task scheduling. This policy operates similarly to a priority dispatch rule and is paired with a Serial Schedule Generation Scheme to produce a schedule. Our empirical evaluation on standard benchmarks demonstrates the approach's superiority in terms of performance and its ability to generalize. The developed framework, Wheatley, is made publicly available online to facilitate further research and reproducibility.

Abstract--Diagnostic medical ultrasound is widely used, safe, and relatively low cost but requires a high degree of expertise to acquire and interpret the images. Personnel with this expertise are often not available outside of larger cities, leading to difficult, costly travel and long wait times for rural populations. T o address this issue, tele-ultrasound techniques are being developed, including robotic teleoperation and recently human teleoperation, in which a novice user is remotely guided in a hand-overhand manner through mixed reality to perform an ultrasound exam. These methods have not been compared, and their relative strengths are unknown. Human teleoperation may be more practical than robotics for small communities due to its lower cost and complexity, but this is only relevant if the performance is comparable. This paper therefore evaluates the differences between human and robotic teleoperation, examining practical aspects such as setup time and flexibility and experimentally comparing performance metrics such as completion time, position tracking, and force consistency. It is found that human teleoperation does not lead to statistically significant differences in completion time or position accuracy, with mean differences of 1.8% and 0.5%, respectively, and provides more consistent force application despite being substantially more practical and accessible. Remote and under-resourced communities have far worse access to healthcare than larger cities [1], [2]. Ultrasound has become one of the most prevalent diagnostic imaging modalities due to its relatively low cost, non-invasive nature, and lack of radiation [3], but many communities have very limited access to qualified sonographers.