Frontiers in Robotics and AI in the AI ethics record

ConceptACT: episode-level concepts for sample-efficient robotic imitation learning — open the original publisher

Imitation learning enables robots to acquire complex manipulation skills from human demonstrations, but current methods rely solely on low-level sensorimotor data while ignoring the rich semantic knowledge humans naturally possess about tasks. We present ConceptACT, an extension of Action Chunking with Transformers that leverages episode-level semantic concept annotations during training to improve learning efficiency. Unlike language-conditioned approaches that require semantic input at deploym

Effect of a robotic insole-type active assist device on horizontal ground reaction force and center-of-pressure stability during stepping in patients with medial knee osteoarthritis — open the original publisher

An insole-type active assist device has been developed as a robotic system to dynamically correct ankle alignment at heel contact in patients with medial knee osteoarthritis. Although our previous feasibility study demonstrated that the device could be safely used during an on-the-spot stepping task, its effects on loading behavior remain unclear. This study aimed to investigate whether dynamic ankle alignment correction using the device alters horizontal ground reaction force variability and ce

Gender and diagnostic differences in children’s preferences for social robot design. A mixed-methods study with autistic and neurotypical children — open the original publisher

IntroductionAutistic children are increasingly engaging with social robots in educational and support contexts, but limited research has compared the perceptual and design preferences of autistic and neurotypical children.MethodsThis mixed-methods study examined the robot design preferences of 43 children aged 3–15 years, including 21 autistic and 22 neurotypical children, and included semi-structured interviews with 11 adult stakeholders, comprising six experts and five non-experts. Quantitativ

PFEA: a VLM-based high-level natural language planning and feedback embodied agent for human-centered AI — open the original publisher

The rapid advancement of Large Language Models (LLMs) has led to significant progress in Artificial Intelligence (AI), ushering in a new era of human-centered AI (HAI). Intelligent agents powered by LLMs provide new opportunities for realizing HAI. However, existing LLM-based embodied agents often lack online planning capabilities and may generate actions involving objects that are not present in the current environment. In this paper, we propose a closed-loop framework for planning and evaluati

Contact-aware multi-skill learning framework with hybrid force-motion control for stability and force regulation in robotic physiotherapy — open the original publisher

Robot-assisted physiotherapy has attracted increasing attention for its potential to provide repeatable, stable, and controllable physical interaction during rehabilitation-oriented therapy. However, contact-rich physiotherapy tasks remain challenging because the robot must reproduce therapist-demonstrated massage skills while adapting to non-planar and deformable body surfaces, suppressing impact during contact transition, and maintaining stable force regulation. This paper proposes a contact-a

Combining exploration and imitation in contact-rich task learning on an articulated soft robot arm — open the original publisher

Learning from demonstration (LfD) has become a popular approach with the emergence of modern transformer-based algorithms. However, the performance of these policies is limited by the quality of the demonstrations. Combining imitation and exploration promises to train policies that perform better and are more reliable. However, this requires a robotic system that can explore safely without damaging itself or the environment, especially in contact-rich tasks during which the robot must exert forc

Robots in mine search and rescue operations: a review of platforms and design requirements — open the original publisher

The use of automation in mining can be found at all stages of the mining process, covering exploration, excavation, loading, transportation, mineral processing, and search and rescue missions in emergency situations. Due to the harsh conditions during an underground mine disaster, robots can be of great assistance to rescue teams by entering areas that are unsafe for human rescuers, locating trapped workers, and collecting valuable data. The design and implementation of coal-mine rescue robots a

Predictive fatigue-aware human-robot collaboration: a real-time, open-source ROS 2 instantiation of a three-module human-centric digital twin framework on a standard cobot — open the original publisher

Industry 5.0 cobotics calls for collaborative robots that adapt to the operator’s physical and cognitive state in real time. Most current industrial deployments remain reactive, responding only after explicit commands and ignoring the operator’s psychophysiological condition. The three-module Human-Centric Digital Twin (HCDT) framework establishes the upstream perception-and-reasoning architecture for such systems using Vision-Language Models, and identifies closed-loop feedback and physical-ass

CoMuRoS - An LLM-based generalizable hierarchical task planning and execution framework for heterogeneous robot teams with event-driven re-planning — open the original publisher

Heterogeneous multi-robot teams require systems that can interpret natural-language goals, allocate tasks, and adapt to unexpected events. We developed CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture combining a centralized task-manager LLM with decentralized robot-level LLMs for executable Python generation from primitive ROS2 skills. The task manager uses static planning rules and dynamic context, including task history, robot/task status, and detected eve

Predictive vision-language monitoring for proactive safety in robot task execution — open the original publisher

Robots that execute language-conditioned tasks in dynamic environments often rely on feedback only after an action has failed, which can be insufficient when failures involve collisions or workspace conflicts. This paper presents a predictive monitoring framework that uses Vision-Language Models (VLMs) to assess near-future execution risk during robot task execution. The framework first generates structured plans with action execution conditions and a plan-level fallback action. During execution

Explainable AI analysis of brake control in CARLA through reference and distilled policies — open the original publisher

This paper presents an explainable AI analysis of brake control in CARLA closed-loop driving. Longitudinal braking is studied through a threshold policy, a risk-aware reference policy, and a distilled policy learned from reference rollouts. The framework combines structured traffic-state features, high-fidelity XGBoost surrogates, and SHAP to analyze deployed brake behavior across Town10HD and Town05. Results show that brake generation is consistently dominated by front-vehicle distance, relativ

Feedback modalities in human-cobot collaboration: experimental evaluation of performance, user experience, and physiological responses — open the original publisher

Collaborative robots (cobots) are increasingly deployed in industrial as well as non-industrial domains to support human-centered operation. While physical safety and task efficiency have received considerable attention, less is known about how feedback modality influences operator experience and physiological responses under different collaboration demands. This study examines the effects of feedback modalities in two human–cobot collaboration scenarios representing distinct coordination struct

Object-grounded embodied picking for e-commerce warehouse fulfillment: a foveated diffusion policy for operational robustness — open the original publisher

Embodied picking for e-commerce fulfillment remains vulnerable to dense clutter, reflective packaging, and background variation, which can undermine the effectiveness and robustness of visuomotor policies learned from demonstrations. A key limitation is the absence of explicit object grounding, causing policies to exploit spurious contextual cues rather than task-relevant visual evidence. To address this issue, we propose the Foveated Diffusion Policy (FDP), which integrates object-centric visua

Learning faults in time: sequential behavioural modelling for complex fault detection in multi-robot systems — open the original publisher

Reliable fault detection in multi-robot systems requires models capable of capturing complex, time-dependent fault signatures that manifest over extended temporal horizons rather than instantaneous observations alone. Existing data-driven approaches operate reactively on behavioural snapshots, failing to capture fault modes whose discriminative signature depends on temporally ordered precursors. This work formalises a theoretical impossibility result demonstrating that memoryless classifiers are

The human-robot interaction scale database — open the original publisher

Crowd navigation in a multi-room environment: a model predictive control framework for mobile robots — open the original publisher

Mobile robots operating in human-populated environments must navigate complex, multi-room spaces while ensuring safety, i.e., generating collision-free motion. In this study, we present a sensor-based model predictive control (MPC) scheme designed for safe crowd navigation in such non-convex environments. The proposed framework decomposes the free space into a set of overlapping convex regions to construct a topological graph, enabling a high-level planner to compute optimal sequences of travers

Robotic lava tube mapping and multimodal data collection using quadruped and LiDAR — open the original publisher

As part of TU Delft Rhizome 2.0 and Moonshot projects, focusing on the development of extraterrestrial habitats in lava tubes, the robotic mapping of an analogue lava tube in Sicily has been studied with the future goal of assessing its suitability for building construction. The main objective of the research was to survey a lava tube and acquire a novel dataset for future research, while also analyzing the collected data to evaluate possible future lava tube exploration scenarios for the Moon a

Editorial: Advanced sensing, learning and control for effective human-robot interaction — open the original publisher

Decoupling what, how, and when for observing decision-making context in autonomous robots — open the original publisher

This paper presents an approach that decouples what to observe, how to observe it, and when observations are required for decision-making in autonomous robots. Situation awareness is essential for efficient and reliable autonomous robot operation, but despite advances toward parallelizing perception and action, key challenges remain in making perception aware of the current context and ensuring observability during action execution. To address this, we explicitly model the decision-making contex

Uncertainty-guided informative path planning for ecological monitoring using autonomous surface vehicles under Dubins motion constraints — open the original publisher

Autonomous surface vehicles (ASVs) enable efficient in-situ data collection for large-scale ecological monitoring; however, effective environmental mapping requires planning strategies that account for not only informative measurements, but also vehicle motion constraints and limited mission resources. Existing approaches often rely on stationary environmental models or loosely coupled planning frameworks that do not fully exploit model uncertainty when generating feasible trajectories. To addre