HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario

• URL: http://arxiv.org/abs/2509.10096v1
• Date: Fri, 12 Sep 2025 09:38:17 GMT
• Title: HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario
• Authors: Saeed Saadatnejad, Reyhaneh Hosseininejad, Jose Barreiros, Katherine M. Tsui, Alexandre Alahi,
• Abstract summary: 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.
• Score: 63.77482302352545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing labor shortage and aging population underline the need for assistive robots to support human care recipients. To enable safe and responsive assistance, robots require accurate human motion prediction in physical interaction scenarios. However, this remains a challenging task due to the variability of assistive settings and the complexity of coupled dynamics in physical interactions. In this work, we address these challenges through two key contributions: (1) HHI-Assist, a dataset comprising motion capture clips of human-human interactions in assistive tasks; and (2) a conditional Transformer-based denoising diffusion model for predicting the poses of interacting agents. Our model effectively captures the coupled dynamics between caregivers and care receivers, demonstrating improvements over baselines and strong generalization to unseen scenarios. By advancing interaction-aware motion prediction and introducing a new dataset, our work has the potential to significantly enhance robotic assistance policies. The dataset and code are available at: https://sites.google.com/view/hhi-assist/home

Related papers

• Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations [63.80827184637476]
  We introduce D-STAR, a hierarchical policy that disentangles when to act from where to act.<n>We validate our framework through extensive and rigorous simulations.
  arXiv  Detail & Related papers  (2026-01-14T14:37:06Z)
• Perceiving and Acting in First-Person: A Dataset and Benchmark for Egocentric Human-Object-Human Interactions [110.43343503158306]
  This paper embeds the manual-assisted task into a vision-language-action framework, where the assistant provides services to the instructor following egocentric vision and commands.<n>Under this setting, we accomplish InterVLA, the first large-scale human-object-human interaction dataset with 11.4 hours and 1.2M frames of multimodal data.<n>We establish novel benchmarks on egocentric human motion estimation, interaction synthesis, and interaction prediction with comprehensive analysis.
  arXiv  Detail & Related papers  (2025-08-06T17:46:23Z)
• 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)
• Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction [9.806227900768926]
  We propose to model social motion forecasting in a shared human-robot representation space. ECHO operates in the aforementioned shared space to predict the future motions of the agents encountered in social scenarios. We evaluate our model in multi-person and human-robot motion forecasting tasks and obtain state-of-the-art performance by a large margin.
  arXiv  Detail & Related papers  (2024-02-07T11:37:14Z)
• IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction [1.3597551064547502]
  Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. New mechanism based on correntropy is introduced to measure the relative importance of human-human interactions.
  arXiv  Detail & Related papers  (2023-11-26T05:17:11Z)
• InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion [29.25063155767897]
  This paper addresses a novel task of anticipating 3D human-object interactions (HOIs) Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.
  arXiv  Detail & Related papers  (2023-08-31T17:59:08Z)
• Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions [5.742409080817885]
  We propose an application of causal discovery methods to model human-robot spatial interactions. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm.
  arXiv  Detail & Related papers  (2022-10-29T08:56:48Z)
• Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions [82.90906153293585]
  We propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.
  arXiv  Detail & Related papers  (2022-06-25T09:55:39Z)
• HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing [1.3678064890824186]
  The Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates.
  arXiv  Detail & Related papers  (2021-10-20T00:41:57Z)
• 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)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

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.