News & Research

arXiv:2607.16501v1 Announce Type: new Abstract: Safe model-based reinforcement learning (RL) often bridges control-theoretic analysis and RL for robots to safely explore (partially) unknown system dynamics while deriving control actions for task efficiency. The control performance and safety assurance typically rely on prior knowledge of partially modeled nominal system dynamics and the data-driven models that compensate for residual model uncertainties. However, existing methods often overlook the structure of residual model uncertainties (e.g., components affine in control), which could lead to overly conservative robot behaviors or invalid safety guarantees under the safe learning-based controllers. This paper proposes a safe reinforcement learning framework that learns control-affine dynamics with a certifiable data-driven safe policy using control barrier functions (CBF). Specifically, we first use Control-Affine Random Fourier Features (ARFF) to model robot dynamics in a control-

arXiv:2607.16471v1 Announce Type: new Abstract: Incremental Nonlinear Dynamic Inversion (INDI) is attractive for unmanned aerial vehicle (UAV) flight control because it reduces dependence on a full aerodynamic model while retaining strong disturbance-rejection capability. For a tilt-rotor vertical takeoff and landing (VTOL) architecture, however, the admissible model-mismatch range of the fast inner loop is still not characterized analytically in a parameter-explicit way. This paper isolates the pitch-rate/elevon subchannel of an existing cascaded INDI controller and studies its linear stability under model mismatch. A closed-form fifth-order transfer function is derived for the full controller-estimator-actuator-plant interconnection, and stability is characterized through the Routh-Hurwitz criterion over a parameterized linear model. Two representative three-parameter sweeps produce interpretable stability regions. Based on these feasibility maps, two uncertainty-aware tuning procedu

arXiv:2607.16428v1 Announce Type: new Abstract: For a second time, the android robot Andrea was set up at a public museum in Germany for six consecutive days to have conversations with visitors, fully autonomously. Building on previously gathered qualitative results, the robot was now capable of engaging in multi-lingual conversation with the visitors about the museum context. The robot was prepared with context information about the museum in general and its surrounding exhibits this time. The robot featured a slightly artificial sounding voice that was previously evaluated as congruent with its gender-ambiguous but very humanlike design. Three experimental conditions were implemented, in which either (1) the robot simulated no emotions, (2) the robots emotions were determined by ChatGPT 4.1, or (3) the WASABI emotion simulation architecture simulated the robot's emotion dynamics. An extended version of the TAM2 questionnaire was employed to let 73 visitors report on several factors o

arXiv:2607.16366v1 Announce Type: new Abstract: Robotic navigation in unstructured environments requires robust situational awareness to safely traverse hazards such as steep slopes and rocky terrain. To address this challenge, perception systems increasingly rely on multimodal sensor fusion. Specifically, integrating thermal imagery with standard optical and depth sensors enhances terrain differentiation, directly improving the reliability of mapping algorithms. This paper presents PRISM, a multimodal perception system for terrain mapping in unstructured settings. PRISM leverages a custom sensor suite to capture aligned RGB, depth, and thermal (RGB-D-T) imagery. At its core is OmniUnet, a novel vision transformer-based network specifically designed for multimodal semantic terrain segmentation. We validated the proposed system using two newly annotated datasets (BASEPROD and LAENTIEC) and demonstrate its real-world applicability through physical field experiments. Deployed on a resourc

arXiv:2607.16341v1 Announce Type: new Abstract: This paper presents SEAGR (Socially and Emotionally Aware Greeting Robot), a robotic greeting framework designed for human-robot interaction environments involving users from diverse cultural backgrounds and different emotional states. Since greeting behaviour strongly influences first impressions, user comfort, and trust, robots operating in public spaces must be able to interact in a socially appropriate and adaptive manner. However, many existing systems still rely on static greeting routines that do not account for cultural variation, emotional context, or interpersonal distance. SEAGR introduces a dual-layer modulation framework in which cultural identity determines the appropriate greeting type, while affective cues influence how that greeting is executed. The system combines context-aware cultural mapping, emotion-based gesture modulation, and proxemic regulation within a unified Sense-Think-Act architecture. A low-cost prototype i

arXiv:2607.16310v1 Announce Type: new Abstract: Motor impairments affecting the upper limb significantly reduce autonomy in daily activities, particularly for tasks involving object manipulation. Assistive robotic arms offer a promising solution, provided they can be controlled in an intuitive, reliable, and responsive manner. Among human--machine interface approaches, surface electromyography (sEMG) enables non-invasive access to muscle activity and thus to the user's motor intentions. This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm. The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN). Several real-time strategies are investigated, including threshold-based onset detection, a two-stage classification approach (rest vs movement followed by gesture recognition), and a single classifier handling both r

arXiv:2607.16302v1 Announce Type: new Abstract: In recent years, motorcycle popularity has been declining, particularly among the younger generations. To rekindle interest in motorcycles among this demographic, we developed a rideable two-wheeled robot equipped with four limbs as a future partner mobility concept and intended for use in public events. The character reproduced by this robot carries the protagonist on its back and exhibits a dynamic quadrupedal gait. To maximize the riding experience, we aimed to match the robot's weight and size to the character's specifications while ensuring rider safety and enabling expressive movements of limbs, wrists, ankles, and facial features. However, achieving locomotion solely through limb movement would require excessive motor output and increased limb strength, resulting in higher weight and extremely slow gait, thereby reducing character fidelity. To overcome these challenges, the robot performs its primary locomotion using a self-balanci

arXiv:2607.16267v1 Announce Type: new Abstract: Continual learning in visual navigation remains challenging due to catastrophic forgetting and the difficulties associated with adapting to diverse and evolving environments. To address these issues, we propose Hyperbolic Dynamic Cluster Memory (HyperDCM), a structure-aware memory mechanism that enhances diffusion policy-based navigation through scene graph modeling and principled memory replay. HyperDCM extracts semantic scene triples from RGB observations using large vision-language models, encodes them into scene graph embeddings via a Relational Graph Convolutional Network (R-GCN), and projects the embeddings into hyperbolic space to enhance structural separability and retention in continual navigation. A dynamic clustering and structure-sensitive update strategy selects representative samples for memory replay, thereby preserving knowledge diversity and mitigating catastrophic forgetting. Experiments on multi-scene indoor and outdoor

arXiv:2603.22507v2 Announce Type: replace Abstract: We present an energy-aware collaborative exploration framework for a UAV-UGV team operating in unknown environments, where the UAV's energy constraint is modeled as a maximum flight-time limit. The UAV executes a sequence of energy-bounded exploration tours, while the UGV simultaneously explores on the ground and serves as a mobile charging station. Rendezvous is enforced under a shared time budget so that the vehicles meet at the end of each tour before the UAV reaches its flight-time limit. We construct a sparsely coupled air-ground roadmap using a density-aware layered probabilistic roadmap (PRM) and formulate tour selection over the roadmap as coupled orienteering problems (OPs) to maximize information gain subject to the rendezvous constraint. The resulting tours are constructed over collision-validated roadmap edges. We validate our method through simulation studies, benchmark comparisons, and real-world experiments.

arXiv:2603.14160v2 Announce Type: replace Abstract: In this paper, we propose a novel framework that allows therapists to teach robot-assisted rehabilitation exercises remotely via RGB-D video. Our system encodes demonstrations as 6-DoF body-centric trajectories using Cartesian Dynamic Movement Primitives (DMPs), ensuring accurate posture-independent spatial generalisation across diverse patient anatomies. Crucially, we execute these trajectories through a decoupled hybrid control architecture that constructs a spatially compliant virtual tunnel, paired with an effort-based temporal dilation mechanism. This architecture is applied to three distinct rehabilitation modalities: Passive, Active-Assisted, and Active-Resistive, by dynamically linking the exercise's execution phase to the patient's tangential force contribution. To guarantee safety, a Gaussian Mixture Regression (GMR) model is learned on-the-fly from the patient's own limb. This allows the detection of abnormal interaction fo

arXiv:2603.09761v2 Announce Type: replace Abstract: High-fidelity visuo-tactile sensing is important for precise robotic manipulation, yet most vision-based tactile sensors rely on opaque coatings that enable tactile sensing but block direct visual observation. We propose MuxGel, a spatially multiplexed sensor that captures both external visual information and contact-induced tactile signals through a single camera. By using a checkerboard coating pattern, MuxGel interleaves tactile-sensitive regions with transparent windows for external vision. This design maintains standard form factors, allowing for plug-and-play integration into GelSight-style sensors by simply replacing the gel pad. To recover dense visual and tactile signals from the multiplexed inputs, we develop a U-Net-based reconstruction framework trained with a sim-to-real pipeline. Experiments on unseen objects demonstrate the framework's generalization and accuracy. We further demonstrate MuxGel in grasping tasks, where v

arXiv:2603.03751v2 Announce Type: replace Abstract: Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks. This paper proposes a bio-inspired, interaction-oriented whole-body control (IO-WBC) that functions as an artificial cerebellum - an adaptive motor agent that translates upstream (skill-level) commands into stable, physically consistent whole-body behavior under contact. This work structurally separates upper-body interaction execution from lower-body support control, enabling the robot to maintain balance while shaping force exchange in a tightly coupled robot-object system. A trajectory-optimized reference generator (RG) provides a kinematic prior, while a reinforcement learning (RL) policy governs body responses under heavy-load interactions and disturbances. The policy is trained in si