We present an interface designed to operate social robots in a highly unpredictable context: a classroom, supporting user-centered design of innovative child–robot interactions. Having a functional prototype enables rich user data elicitation and analysis, essential for understanding user needs and deriving meaningful requirements. Deploying robots outside controlled laboratory conditions into a classroom, however, introduces challenges related to safety, robustness, and managing multiple, often noisy, interactions. Our system addresses these challenges by providing a safe, flexible and resilient interface that ensures safe operation while allowing improvisation and adaptability to unpredictable children's behaviors. The interface aims to be intuitive for non-expert users and support everyday teaching and learning activities in the classrooms. By prioritizing usability, modularity, and robustness, our approach facilitates iterative design, accelerates the transition from Wizard-of-Oz prototyping to autonomous behaviors, and contributes to making child–robot interaction technologies more accessible and practical for diverse application domains.

Performance-evaluation pipelines in systems research often combine benchmarks, system configuration steps, profiling tools, and analysis scripts. In practice, these components are glued together with ad-hoc shell scripts, notebooks, and bespoke tooling, making experiment dimensions difficult to explore systematically and results hard to reproduce or extend. We present benchkit, a lightweight Python library that provides a structured way to express performance experiments declaratively and to automate their full lifecycle—from build and execution to system configuration, profiling, and result collection. Instead of relying on monolithic scripts, benchkit provides a structured way to compose existing system tools (e.g., CPU-placement utilities, frequency controllers, and performance profilers) while keeping benchmark code untouched. We illustrate benchkit through two representative studies: (1) a drilldown of performance anomalies in SPEC CPU workloads on hybrid-core x86 processors, enabled by systematic exploration of CPU placement policies; and (2) an analysis of lock implementations and scheduling strategies on a many-core ARM server, where benchkit coordinates system tools and visualizations to interpret performance differences. We evaluate the overhead of benchkit and show that it introduces no measurable cost compared to hand-written shell workflows, both on the host and inside containers. These results show that benchkit provides a reproducible, extensible, and principled foundation for system-level performance experimentation.

Palm-sized autonomous nano-drones, i.e., sub-50 g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demanding more efficient and lightweight vision-based pipelines to win the race. This work presents a map-free vision-based (using only a monocular camera) autonomous nano-drone that combines a real-time deep learning gate detection front-end with a classic yet elegant and effective visual servoing control back-end, only relying on onboard resources. Starting from two state-of-the-art tiny deep learning models, we adapt them for our specific task, and after a mixed simulator-real-world training, we integrate and deploy them aboard our nano-drone. Our best-performing pipeline costs of only 24 M multiply-accumulate operations per frame, resulting in a closed-loop control performance of 30 Hz, while achieving a gate detection root mean square error of 1.4 pixels, on our ~20 k real-world image dataset. In-field experiments highlight the capability of our nano-drone to successfully navigate through 15 gates in 4 min, never crashing and covering a total travel distance of ~ 100 m, with a peak flight speed of 1.9 m/s. Finally, to stress the generalization capability of our system, we also test it in a never-seen-before environment, where it navigates through gates for more than 4 min.

The automatic translation of legacy C code to Rust presents significant challenges, particularly in handling preprocessor macros. C macros introduce metaprogramming constructs that operate at the text level, outside of C's syntax tree, making their direct translation to Rust non-trivial. Existing transpilers --- source-to-source compilers --- expand macros before translation, sacrificing their abstraction and reducing code maintainability. In this work, we introduce Oxidize, a macro-aware C-to-Rust transpilation framework that preserves macro semantics by translating C macros into Rust-compatible constructs while selectively expanding only those that interfere with Rust's stricter semantics. We evaluate our techniques on a small-scale study of real-world macros and find that the majority can be safely and idiomatically transpiled without full expansion.

The detection of mutual gaze in the context of human-robot interaction is crucial for the understanding of human partners' behavior. Indeed, the monitoring of the users' gaze from a long distance enables the prediction of their intention and allows the robot to be proactive. Nonetheless, current implementations struggle or cannot operate in scenarios where detection from long distances is required. In this work, we propose a ROS2 software pipeline that detects mutual gaze up to 5 m of distance. The code relies on robust off-the-shelf perception algorithms.

For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person’s gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5 % to 91.2 %); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system’s ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.

We consider a service robot that offers chocolate treats to people passing in its proximity: it has the capability of predicting in advance a person’s intention to interact, and to actuate an "offering" gesture, subtly extending the tray of chocolates towards a given target. We run the system for more than 5 hours across 3 days and two different crowded public locations; the system implements three possible behaviors that are randomly toggled every few minutes: passive (e.g. never performing the offering gesture); or active, triggered by either a naive distance-based rule, or a smart approach that relies on various behavioral cues of the user. We collect a real-world dataset that includes information on 1777 users with several spontaneous human-robot interactions and study the influence of robot actions on people’s behavior. Our comprehensive analysis suggests that users are more prone to engage with the robot when it proactively starts the interaction. We release the dataset and provide insights to make our work reproducible for the community. Also, we report qualitative observations collected during the acquisition campaign and identify future challenges and research directions in the domain of social human-robot interaction.

Nowadays ubiquitous robots must be adaptive and easy to use. To this end, dynamical system-based imitation learning plays an important role. In fact, it allows to realize stable and complex robotic tasks without explicitly coding them, thus facilitating the robot use. However, the adaptation capabilities of dynamical systems have not been fully exploited due to the lack of closed-loop implementations making use of visual feedback. In this regard, the integration of visual information allows higher flexibility to cope with environmental changes. This work presents a dynamical system-based imitation learning for visual servoing, based on the large projection task priority formulation. The proposed scheme enables complex and stable visual tasks, as demonstrated by a simulation analysis and experiments with a robotic manipulator.

We introduce PointIt, a toolkit for the Robot Operating System (ROS2) to build human-robot interfaces based on pointing gestures sensed by a wrist-worn Inertial Measurement Unit, such as a smartwatch. We release the software as open-source with MIT license; docker images and exhaustive instructions simplify its usage in simulated and real-world deployments.

We present an interactive demonstration where users are immersed in a virtual reality simulation of a logistic automation system. Using pointing gestures sensed by wrist-worn inertial measurement unit, users select defective packages transported on conveyor belts. The demonstration allows users to experience a novel way to interact with automation systems, and shows an effective application of virtual reality for human-robot interaction studies.

This work presents the dual benefit of integrating imitation learning techniques, based on the dynamical systems formalism, with the visual servoing paradigm. On the one hand, dynamical systems allow to program additional skills without explicitly coding them in the visual servoing law, but leveraging few demonstrations of the full desired behavior. On the other, visual servoing allows to consider exteroception into the dynam-ical system architecture and be able to adapt to unexpected environment changes. The beneficial combination of the two concepts is proven by applying three existing dynamical systems methods to the visual servoing case. Simulations validate and compare the methods; experiments with a robot manipulator show the validity of the approach in a real-world scenario.

An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.

Implementing highly efficient and correct synchronization primitives on modern Weak Memory Model (WMM) architectures, such as ARM and RISC-V, is very difficult even for human experts. We introduce VSync, a framework to assist in optimizing and verifying synchronization primitives on WMM architectures. VSync automatically detects missing and overly-constrained barriers, while ensuring essential safety and liveness properties. VSync relies on two novel techniques: 1) Adaptive Linear Relaxation (ALR), which utilizes barrier monotonicity and speculation to quickly find a correct maximally-relaxed barrier combination; and 2) Await Model Checking (AMC), which for the first time makes it possible to check termination of await loops on WMMs.

The big potential of humanoid robots is not restricted to the ground, but these versatile machines can be successfully employed in unconventional scenarios, e.g. space, where contacts are not always present. In these situations, the robot’s limbs can be used to assist or even generate the angular motion of the floating base, as a consequence of the centroidal momentum conservation. In this paper, we propose to combine, in the same whole-body motion control, visual servoing and centroidal momentum conservation. The former dictates a rotation to the floating humanoid to achieve a task in the Cartesian space; the latter is exploited to realize the desired rotation by moving the robot’s articulations. Simulations in a space scenario are carried out using COMAN, a humanoid robot developed at the Istituto Italiano di Tecnologia.

Efficient locking mechanisms are extremely important to support large-scale concurrency and exploit the performance promises of many-core servers. Implementing an efficient, generic, and correct lock is very challenging due to the differences between various NUMA architectures. The performance impact of architectural/NUMA hierarchy differences between x86 and Armv8 are not yet fully explored, leading to unexpected performance when simply porting NUMA-aware locks from x86 to Armv8. Moreover, due to the Armv8 Weak Memory Model (WMM), correctly implementing complicated NUMA-aware locks is very difficult.

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

The demands for high performance computing with a low cost and low power consumption are driving a transition towards multi-core processors in many consumer and industrial applications. However, the adoption of multi-core processors in the domain of real-time systems faces a series of challenges that has been the focus of great research intensity during the last decade. These challenges arise in great part from the non real-time nature of the hardware arbiters that schedule the access to shared resources, such as the main memory. One solution proposed in the literature is called Memory Centric Scheduling, which defines a separate software scheduler for the sections of the tasks that will access the main memory, hence circumventing the low level unpredictable hardware arbiters. Several Memory Centric schedulers and associated theoretical analyses have been proposed, but as far as we know, no actual implementation of the required OS-level underpinnings to support dynamic event-driven Memory Centric Scheduling has been presented before. In this paper we aim to fill this gap, targeting cache based COTS multi-core systems. We will confirm via measurements the main theoretical benefits of Memory Centric Scheduling (e.g. task isolation). Furthermore, we will describe an effective schedulability analysis using concepts from distributed systems.

In this paper we propose a method to perform manual guidance with humanoid robots. Manual guidance is a general model of physical interaction: here we focus on guiding a humanoid by its hands. The proposed technique can be, however, used also for joint object transportation and other tasks implying human-humanoid physical interaction. Using a measure of the Instantaneous Capture Point, we develop an equilibrium-based interaction technique that does not require force/torque or vision sensors. It is, therefore, particularly suitable for low-cost humanoids and toys. The proposed method has been experimentally validated on the small humanoid NAO.

In this work, we investigate the potential benefit of parallelization for both meeting real-time constraints and minimizing power consumption. We consider malleable Gang scheduling of implicit-deadline sporadic tasks upon multiprocessors. By extending schedulability criteria for malleable jobs to DPM/DVFS-enabled multiprocessor platforms, we are able to derive an offline polynomial-time optimal processor/frequency-selection algorithm. Simulations of our algorithm on randomly generated task systems executing on platforms having up to 16 processing cores show that the theoretical power consumption is reduced by a factor of 36 compared to the optimal non-parallel approach.

We present a control-based approach for visual navigation of humanoid robots in office-like environments. In particular, the objective of the humanoid is to follow a maze of corridors, walking as close as possible to their center to maximize motion safety. Our control algorithm is inspired by a technique originally designed for unicycle robots and extended here to cope with the presence of turns and junctions. The feedback signals computed for the unicycle are transformed to inputs that are suited for the locomotion system of the humanoid, producing a natural, human-like behavior. Experimental results for the humanoid robot NAO are presented to show the validity of the approach, and in particular the successful extension of the controller to turns and junctions.

The article presents an improved formulation of an existing model predictive control scheme used to generate online "stable" walking motions for a humanoid robot. We introduce: (i) a change of variable that simplifies the optimiza tion problem to be solved; (ii) a simply bounded formulation in the case when the positions of the feet are predetermined; (iii) a formulation allowing foot repositioning (when the system is perturbed) based on ℓ1- and ℓ∞-norm minimization; (iv) a formulation that accounts for (approximate) double support constraints when foot repositioning occurs.