arXiv subjects

Matthew K. X. J. Pan

Publications and source records attributed to Matthew K. X. J. Pan.

3 recordsLinked to original sources

arXiv2026

ETHOS: A Robotic Encountered-Type Haptic Display for Social Interaction in Virtual Reality

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 28.53 +/- 31.21 ms across all interaction and control conditions. 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.

cs.RO↗

arXiv2026

Exploring Immersive Social-Physical Interaction with Virtual Characters through Coordinated Robotic Encountered-Type Contact

This work presents novel robot-mediated immersive experiences enabled by an encountered-type haptic display (ETHD) that introduces direct physical contact in virtual environments. We focus on social-physical interactions, a class of interaction associated with meaningful human outcomes in prior human-robot interaction (HRI) research. We explore the implementation of this interaction paradigm in immersive virtual environments through an object handover, fist bump, and high five with a virtual character. Extending this HRI paradigm into immersive environments enables the study of how physically grounded robotic contact and virtual augmentation jointly shape these novel social-physical interaction experiences. To support this investigation, we introduce ETHOS (Encountered-Type Haptics for On-demand Social interaction), an experimental platform integrating a torque-controlled manipulator and interchangeable props with a headset-mediated virtual experience. ETHOS enables co-located physical interaction through marker-based physical-virtual registration while concealing the robot behind the virtual environment, decoupling contact from visible robot embodiment. Both technical characterization, through spatial alignment and interaction latency tests, and experiential evaluation, through a 55 participant user study, were completed. Overall, the findings demonstrate the feasibility and experiential value of robot-mediated social-physical interaction in VR and motivate further development of dynamic encountered-type approaches for immersive HRI.

cs.HC↗

arXiv2026

Supercomputing for High-speed Avoidance and Reactive Planning in Robots

This paper presents SHARP (Supercomputing for High-speed Avoidance and Reactive Planning), a proof-of-concept study demonstrating how high-performance computing (HPC) can enable millisecond-scale responsiveness in robotic control. While modern robots face increasing demands for reactivity in human-robot shared workspaces, onboard processors are constrained by size, power, and cost. Offloading to HPC offers massive parallelism for trajectory planning, but its feasibility for real-time robotics remains uncertain due to network latency and jitter. We evaluate SHARP in a stress-test scenario where a 7-DOF manipulator must dodge high-speed foam projectiles. Using a hash-distributed multi-goal A* search implemented with MPI on both local and remote HPC clusters, the system achieves mean planning latencies of 22.9 ms (local) and 30.0 ms (remote, ~300 km away), with avoidance success rates of 84% and 88%, respectively. These results show that when round-trip latency remains within the tens-of-milliseconds regime, HPC-side computation is no longer the bottleneck, enabling avoidance well below human reaction times. The SHARP results motivate hybrid control architectures: low-level reflexes remain onboard for safety, while bursty, high-throughput planning tasks are offloaded to HPC for scalability. By reporting per-stage timing and success rates, this study provides a reproducible template for assessing real-time feasibility of HPC-driven robotics. Collectively, SHARP reframes HPC offloading as a viable pathway toward dependable, reactive robots in dynamic environments.

cs.RO↗