# Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

arXiv: https://arxiv.org/abs/1802.10503 · HTML: https://mlsanity.com/paper/1802.10503/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 12 of 12)

- [SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos](https://mlsanity.com/paper/2109.00829/) (2021)
- [Gaze-based intention estimation: principles, methodologies, and applications in HRI](https://mlsanity.com/paper/2302.04530/) (2023)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation](https://mlsanity.com/paper/2210.05991/) (2022)
- [Anticipate & Act : Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments](https://mlsanity.com/paper/2502.02066/) (2025)
- [When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor](https://mlsanity.com/paper/2602.18850/) (2026)
- [Real-Time Tactile Grasp Force Sensing Using Fingernail Imaging via Deep Neural Networks](https://mlsanity.com/paper/2109.15231/) (2021)
- [HiMemFormer: Hierarchical Memory-Aware Transformer for Multi-Agent Action Anticipation](https://mlsanity.com/paper/2411.01455/) (2024)
- [Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers](https://mlsanity.com/paper/2203.00156/) (2022)
- [Intention Action Anticipation Model with Guide-Feedback Loop Mechanism](https://mlsanity.com/paper/2403.12450/) (2024)
- [Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks](https://mlsanity.com/paper/2403.19770/) (2024)

## Abstract (arXiv)

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipation