# Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks

arXiv: https://arxiv.org/abs/1908.08391 · HTML: https://mlsanity.com/paper/1908.08391/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding](https://mlsanity.com/paper/2401.08399/) (2024)
- [GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities](https://mlsanity.com/paper/2412.04244/) (2024)
- [ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching](https://mlsanity.com/paper/2409.04576/) (2024)
- [Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos](https://mlsanity.com/paper/2207.09425/) (2022)
- [Learning Asynchronous and Sparse Human-Object Interaction in Videos](https://mlsanity.com/paper/2103.02758/) (2021)
- [Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks](https://mlsanity.com/paper/2403.03270/) (2024)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos](https://mlsanity.com/paper/2310.00670/) (2023)
- [Ins-HOI: Instance Aware Human-Object Interactions Recovery](https://mlsanity.com/paper/2312.09641/) (2023)
- [Understanding Spatio-Temporal Relations in Human-Object Interaction using Pyramid Graph Convolutional Network](https://mlsanity.com/paper/2410.07912/) (2024)
- [How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks](https://mlsanity.com/paper/2306.05844/) (2023)
- [Multi Sentence Description of Complex Manipulation Action Videos](https://mlsanity.com/paper/2311.07285/) (2023)
- [From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2407.00917/) (2024)
- [Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection](https://mlsanity.com/paper/2304.09789/) (2023)
- [Understanding Human Activity with Uncertainty Measure for Novelty in Graph Convolutional Networks](https://mlsanity.com/paper/2410.07917/) (2024)
- [Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation](https://mlsanity.com/paper/2501.15071/) (2025)
- [Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2506.03440/) (2025)
- [Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living](https://mlsanity.com/paper/2208.01910/) (2022)
- [Classification of Primitive Manufacturing Tasks from Filtered Event Data](https://mlsanity.com/paper/2303.09558/) (2023)
- [A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction](https://mlsanity.com/paper/2110.13280/) (2021)
- [Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction](https://mlsanity.com/paper/2307.12729/) (2023)
- [Using The Concept Hierarchy for Household Action Recognition](https://mlsanity.com/paper/2409.08853/) (2024)
- [Towards Open-World Human Action Segmentation Using Graph Convolutional Networks](https://mlsanity.com/paper/2507.00756/) (2025)
- [Grasp Like Humans: Learning Generalizable Multi-Fingered Grasping from Human Proprioceptive Sensorimotor Integration](https://mlsanity.com/paper/2509.08354/) (2025)
- [Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration](https://mlsanity.com/paper/2511.08732/) (2025)
- [Real-Time Manipulation Action Recognition with a Factorized Graph Sequence Encoder](https://mlsanity.com/paper/2503.12034/) (2025)
- [Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation](https://mlsanity.com/paper/2507.00752/) (2025)
- [Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks](https://mlsanity.com/paper/2508.13675/) (2025)
- [Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning](https://mlsanity.com/paper/2601.11460/) (2026)

## Abstract (arXiv)

Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused on the overall prevalent action being executed, but we argue that bimanual human motion cannot always be described sufficiently with a single action label. We present a system for frame-wise action classification and segmentation in bimanual human demonstrations. The system extracts symbolic spatial object relations from raw RGB-D video data captured from the robot's point of view in order to build graph-based scene representations. To learn object-action relations, a graph network classifier is trained using these representations together with ground truth action labels to predict the action executed by each hand. We evaluated the proposed classifier on a new RGB-D video dataset showing daily action sequences focusing on bimanual manipulation actions. It consists of 6 subjects performing 9 tasks with 10 repetitions each, which leads to 540 video recordings with 2 hours and 18 minutes total playtime and per-hand ground truth action labels for each frame. We show that the classifier is able to reliably identify (action classification macro F1-score of 0.86) the true executed action of each hand within its top 3 predictions on a frame-by-frame basis without prior temporal action segmentation.