The large-scale propagation of human-human handover research has been hindered by two challenges.

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.

Safe and trustworthy Human Robot Interaction (HRI) requires robots not only to complete tasks but also to regulate impedance and speed according to scene context and human proximity. We present SafeHumanoid, an egocentric vision pipeline that links Vision Language Models (VLMs) with Retrieval-Augmented Generation (RAG) to schedule impedance and velocity parameters for a humanoid robot. Egocentric frames are processed by a structured VLM prompt, embedded and matched against a curated database of validated scenarios, and mapped to joint-level impedance commands via inverse kinematics. We evaluate the system on tabletop manipulation tasks with and without human presence, including wiping, object handovers, and liquid pouring. The results show that the pipeline adapts stiffness, damping, and speed profiles in a context-aware manner, maintaining task success while improving safety. Although current inference latency (up to 1.4 s) limits responsiveness in highly dynamic settings, SafeHumanoid demonstrates that semantic grounding of impedance control is a viable path toward safer, standard-compliant humanoid collaboration.

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.

This work investigates how robot-mediated physicality influences the perception of social-physical interactions with virtual characters. ETHOS (Encountered-Type Haptics for On-demand Social interaction) is an encountered-type haptic display that integrates a torque-controlled manipulator and interchangeable props with a VR headset to enable three gestures: object handovers, fist bumps, and high fives. We conducted a user study to examine how ETHOS adds physicality to virtual character interactions and how this affects presence, realism, enjoyment, and connection metrics. Each participant experienced one interaction under three conditions: no physicality (NP), static physicality (SP), and dynamic physicality (DP). SP extended the purely virtual baseline (NP) by introducing tangible props for direct contact, while DP further incorporated motion and impact forces to emulate natural touch. Results show presence increased stepwise from NP to SP to DP. Realism, enjoyment, and connection also improved with added physicality, though differences between SP and DP were not significant. Comfort remained consistent across conditions, indicating no added psychological friction. These findings demonstrate the experiential value of ETHOS and motivate the integration of encountered-type haptics into socially meaningful VR experiences.

ETHOS (Encountered-Type Haptics for On-demand Social interaction) enables corresponding virtual and physical renderings of dynamic interpersonal interactions, demonstrated here with an object handover (left), fist bump (centre), and high five (right). Abstract-- We present ETHOS (Encountered-Type Haptics for On-demand Social interaction), a dynamic encountered-type haptic display (ETHD) that enables natural physical contact in virtual reality (VR) during social interactions such as handovers, fist bumps, and high-fives. The system integrates a torque-controlled robotic manipulator with interchangeable passive props (silicone hand replicas and a baton), marker-based physical-virtual registration via a ChArUco board, and a safety monitor that gates motion based on the user's head and hand pose. We introduce two control strategies: (i) a static mode that presents a stationary prop aligned with its virtual counterpart, consistent with prior ETHD baselines, and (ii) a dynamic mode that continuously updates prop position by exponentially blending an initial mid-point trajectory with real-time hand tracking, generating a unique contact point for each interaction. Bench tests show static colocation accuracy of 5.09 0.94 mm, while user interactions achieved temporal alignment with an average contact latency of 28.58 31.21 These results demonstrate the feasibility of recreating socially meaningful haptics in VR. By incorporating essential safety and control mechanisms, ETHOS establishes a practical foundation for high-fidelity, dynamic interpersonal interactions in virtual environments. I. INTRODUCTION Virtual reality (VR) enables embodied engagement with digital environments and creates immersive experiences that unlock novel affordances. Advances in hardware and content creation over the past decade have driven increasing interest in the field, supporting the adoption of VR across a broad range of domains.

Abstract--Surgical suturing is a high-precision task that impacts patient healing and scarring. Suturing skill varies widely between surgeons, highlighting the need for robot assistance. Previous robot suturing works, such as STITCH 1.0 [1], struggle to fully close wounds due to inaccurate needle tracking and poor thread management. T o address these challenges, we present STITCH 2.0, an elevated augmented dexterity pipeline with seven improvements including: improved EKF needle pose estimation, new thread untangling methods, and an automated 3D suture alignment algorithm. Experimental results over 15 trials find that STITCH 2.0 on average achieves 74.4% wound closure with 4.87 sutures per trial, representing 66% more sutures in 38% less time compared to the previous baseline. When two human interventions are allowed, STITCH 2.0 averages six sutures with 100% wound closure rate. URGICAL robots have revolutionized minimally invasive surgery, with Intuitive Surgical's da Vinci system performing over 2.6 million procedures in 2024 [2]. While these procedures require complete human control, recent advances in artificial intelligence (AI) present opportunities for surgical robot autonomy. However, the high-risk nature of surgery raises safety concerns for fully autonomous AI systems.

The emerging paradigm of 6G multiple Radio Access Technology (multi-RAT) networks, where cellular and Wireless Fidelity (WiFi) transmitters coexist, requires mobility decisions that remain reliable under fast channel dynamics, interference, and heterogeneous coverage. Handover in multi-RAT deployments is still highly reactive and event-triggered, relying on instantaneous measurements and threshold events. This work proposes a Machine Learning (ML)-assisted Predictive Conditional Handover (P-CHO) framework based on a model-driven and short-horizon signal quality forecasts. We present a generalized P-CHO sequence workflow orchestrated by a RAT Steering Controller, which standardizes data collection, parallel per-RAT predictions, decision logic with hysteresis-based conditions, and CHO execution. Considering a realistic multi-RAT environment, we train RAT-aware Long Short Term Memory (LSTM) networks to forecast the signal quality indicators of mobile users along randomized trajectories. The proposed P-CHO models are trained and evaluated under different channel models for cellular and IEEE 802.11 WiFi integrated coverage. We study the impact of hyperparameter tuning of LSTM models under different system settings, and compare direct multi-step versus recursive P-CHO variants. Comparisons against baseline predictors are also carried out. Finally, the proposed P-CHO is tested under soft and hard handover settings, showing that hysteresis-enabled P-CHO scheme is able to reduce handover failures and ping-pong events. Overall, the proposed P-CHO framework can enable accurate, low-latency, and proactive handovers suitable for ML-assisted handover steering in 6G multi-RAT deployments.

Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we introduce HANDO (Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation), a two-layer framework designed for legged robots equipped with manipulators to perform human-centered mobile manipulation tasks. The first layer utilizes a goal-conditioned autonomous exploration policy to guide the robot to semantically specified targets, such as a black office chair in a dynamic environment. The second layer employs a unified whole-body loco-manipulation policy to coordinate the arm and legs for precise interaction tasks-for example, handing a drink to a person seated on the chair. We have conducted an initial deployment of the navigation module, and will continue to pursue finer-grained deployment of whole-body loco-manipulation.