Authors: Alihan Bakir, Ekrem Yüksel, Fabio Zuliani, Neil Chennoufi, Francesco Bruno, Jamie Paik
Abstract
Humanity is at the forefront of yet another digital revolution, where the lines between real and virtual worlds are dissolving, reshaping how we perceive and interact with our surroundings. In this context, we introduce a transformative paradigm for immersive virtual experiences centered around whole-body kinetic interactions. Our approach redefines immersion through three distinct levels: audio-visual immersion, capturing sensory realism; physical immersion, delivering haptic feedback; and full-body immersion (FBI), where dynamic bodily interaction integrates seamlessly with virtual environments. At the core of this innovation lies a scalable, distributable platform based on modular robotic surface units inspired by the adaptive designs of nature. These units enable the rendering of immersive environments at any scale, from intimate personal experiences to expansive multi-user settings, dynamically adapting to interactions in real-time. The modular system distributes force, shape, and motion feedback throughout entire spaces, replicating the physical characteristics of the environment and enabling new depth of engagement through FBI. By combining scalability, adaptability, and dynamic physical engagement, this framework bridges the gap between real and virtual worlds. It offers an unprecedented level of immersion where users can engage their entire bodies in symbiotic interactions with the virtual space. This work not only advances immersive technology but also redefines how humans and virtual environments coexist, setting a foundation for a new era of human-environment synthesis.
Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environments. Although recent advancements have significantly enhanced the agility of individual robots, bridging the gap to physically coupled multi-humanoid interaction remains challenging, primarily due to severe kinematic mismatches and complex contact dynamics. To address this, we introduce Rhythm, the first unified framework enabling real-world deployment of dual-humanoid systems for complex, physically plausible interactions. Our framework integrates three core components: (1) an Interaction-Aware Motion Retargeting (IAMR) module that generates feasible humanoid interaction references from human data; (2) an Interaction-Guided Reinforcement Learning (IGRL) policy that masters coupled dynamics via graph-based rewards; and (3) a real-world deployment system that enables robust transfer of dual-humanoid interaction. Extensive experiments on physical Unitree G1 robots demonstrate that our framework achieves robust interactive whole-body control, successfully transferring diverse behaviors such as hugging and dancing from simulation to reality.
This paper presents an immersive bilateral teleoperation framework for beyond-human-scale manipulators, combining motion/force transparency with enhanced operator embodiment through virtual reality (VR) and distributed haptic feedback. The platform integrates a full-scale industrial hydraulic manipulator, a 7-DoF haptic exoskeleton, and head-tracked visual feedback to reinforce operator agency and self-location. A force-sensorless adaptive controller incorporates an augmented human-robot dynamic model to address master-surrogate asymmetries, input nonlinearities, model uncertainties, and communication delays. Rigorous analysis establishes semi-global uniform ultimate boundedness of the closed-loop system. Extensive full-scale experiments demonstrate precise motion and force tracking with scaling up to 1:13 and 1:1000, respectively, and fixed or time-varying delays up to 150 ms. Coordinated 6-DoF pick-and-place and multi-axis contact tasks further validate system performance. A ten-participant user study supports system usability and indicates approximately 50% higher normalized embodiment scores with VR than with monitor-based feedback. These results advance intuitive, force-reflected remote manipulation at industrial scale.
Accurately reconstructing full-body poses from sparse head and hand trajectories is a foundational challenge for immersive AR/VR telepresence. Current methods often struggle with error accumulation and unnatural joint coordination, primarily because they treat the human body as a monolithic entity, thereby failing to capture the fine-grained ``atomic intents'' embedded in subtle signal variations and overlooking the inherent structural topology. To bridge this gap, we present AtomicMotion, a framework designed to decouple and re-integrate body dynamics through three core innovations. First, we introduce a logical body partitioning scheme that decomposes the skeleton into five distinct clusters based on functional intent; this ensures that each partition preserves internal joint synergies while isolating local motion primitives. Second, to robustly map sparse inputs to high-dimensional poses, we employ a masked full-body pre-conditioning strategy during training, forcing the model to internalize global skeletal topology and latent kinematic constraints. Finally, addressing the limitations of vanilla spatial attention, which often ignores fixed physiological connectivity, we propose Kinematic Attention. By embedding the classical kinematic tree structure into the attention mechanism, we ensure biological plausibility in the synthesized motions. Extensive evaluations on the AMASS dataset demonstrate that AtomicMotion significantly outperforms existing baselines, yielding higher reconstruction fidelity and superior biomechanical realism.