Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next
- URL: http://arxiv.org/abs/2512.07765v1
- Date: Mon, 08 Dec 2025 17:47:19 GMT
- Title: Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next
- Abstract summary: Physical Human-Humanoid Interaction (pHHI) is a rapidly advancing field with significant implications for deploying robots in unstructured, human-centric environments.<n>In this review, we examine the current state of the art in pHHI through three core pillars: (i) humanoid modeling and control, (ii) human intent estimation, and (iii) computational human models.
- Score: 4.748609364020611
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Physical Human-Humanoid Interaction (pHHI) is a rapidly advancing field with significant implications for deploying robots in unstructured, human-centric environments. In this review, we examine the current state of the art in pHHI through three core pillars: (i) humanoid modeling and control, (ii) human intent estimation, and (iii) computational human models. For each pillar, we survey representative approaches, identify open challenges, and analyze current limitations that hinder robust, scalable, and adaptive interaction. These include the need for whole-body control strategies capable of handling uncertain human dynamics, real-time intent inference under limited sensing, and modeling techniques that account for variability in human physical states. Although significant progress has been made within each domain, integration across pillars remains limited. We propose pathways for unifying methods across these areas to enable cohesive interaction frameworks. This structure enables us not only to map the current landscape but also to propose concrete directions for future research that aim to bridge these domains. Additionally, we introduce a unified taxonomy of interaction types based on modality, distinguishing between direct interactions (e.g., physical contact) and indirect interactions (e.g., object-mediated), and on the level of robot engagement, ranging from assistance to cooperation and collaboration. For each category in this taxonomy, we provide the three core pillars that highlight opportunities for cross-pillar unification. Our goal is to suggest avenues to advance robust, safe, and intuitive physical interaction, providing a roadmap for future research that will allow humanoid systems to effectively understand, anticipate, and collaborate with human partners in diverse real-world settings.
Related papers
- GNOCHI: Generative Neural mOdel for Close Human-Human Interactions [7.759879304243429]
This paper introduces a novel generative model for close 3D human-human interactions using a conditional variational autoencoder (cVAE)<n>We address two underlying long-standing challenges in the field of human-human interaction: data scarcity and collision awareness.<n>We demonstrate a wide variety of plausible and physically correct interactions, not possible to generate with current state-of-the-art methods.
arXiv Detail & Related papers (2026-07-11T17:19:32Z) - HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
arXiv Detail & Related papers (2025-09-12T09:38:17Z) - ECHO: Ego-Centric modeling of Human-Object interactions [71.17118015822699]
ECHO (Ego-Centric modeling of Human-Object interactions) is developed.<n>It recovers three modalities: human pose, object motion, and contact from such minimal observation.<n>It outperforms existing methods that do not offer the same flexibility.
arXiv Detail & Related papers (2025-08-29T12:12:22Z) - Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
arXiv Detail & Related papers (2025-08-04T06:35:48Z) - 3D Human Interaction Generation: A Survey [25.736432845850576]
3D human interaction generation focuses on producing dynamic and contextually relevant interactions between humans and interactive entities.<n>Recent advancements in 3D model representation methods, motion capture technologies, and generative models have laid a solid foundation for the growing interest in this domain.<n>Despite the rapid advancements in this area, challenges remain due to the need for naturalness in human motion generation and the accurate interaction between humans and interactive entities.
arXiv Detail & Related papers (2025-03-17T12:47:33Z) - A Survey on Human Interaction Motion Generation [17.364321072349004]
Humans inhabit a world defined by interactions -- with other humans, objects, and environments.<n>Interactive movements convey our relationships with our surroundings and demonstrate how we perceive and communicate with the real world.<n> replicating these interaction behaviors in digital systems has emerged as an important topic for applications in robotics, virtual reality, and animation.
arXiv Detail & Related papers (2025-03-17T02:55:10Z) - Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues.
Our method outperforms the representative models regarding objective metrics and visual quality.
arXiv Detail & Related papers (2024-10-15T07:35:51Z) - Position: Towards Bidirectional Human-AI Alignment [109.57781720848669]
We argue that the research community should explicitly define and critically reflect on "alignment" to account for the bidirectional and dynamic relationship between humans and AI.<n>We introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI.
arXiv Detail & Related papers (2024-06-13T16:03:25Z) - Expressive Forecasting of 3D Whole-body Human Motions [38.93700642077312]
We are the first to formulate a whole-body human pose forecasting framework.
Our model involves two key constituents: cross-context alignment (XCA) and cross-context interaction (XCI)
We conduct extensive experiments on a newly-introduced large-scale benchmark and achieve state-of-theart performance.
arXiv Detail & Related papers (2023-12-19T09:09:46Z) - Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
ConCue is a novel approach for improving visual feature extraction in HOI detection.
We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
arXiv Detail & Related papers (2023-11-26T09:11:32Z) - A Mental-Model Centric Landscape of Human-AI Symbiosis [31.14516396625931]
We introduce a significantly general version of human-aware AI interaction scheme, called generalized human-aware interaction (GHAI)
We will see how this new framework allows us to capture the various works done in the space of human-AI interaction and identify the fundamental behavioral patterns supported by these works.
arXiv Detail & Related papers (2022-02-18T22:08:08Z) - TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
TRiPOD is a novel method for predicting body dynamics based on graph attentional networks.
To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame.
Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
arXiv Detail & Related papers (2021-04-08T20:01:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.