History Encoding Representation Design for Human Intention Inference
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Abstract (arXiv)
In this extended abstract, we investigate the design of learning representation for human intention inference. In our designed human intention prediction task, we propose a history encoding representation that is both interpretable and effective for prediction. Through extensive experiments, we show our prediction framework with a history encoding representation design is successful on the human intention prediction problem.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 11 references that are in the map. First posted on arXiv 4 Jun 2021.
Builds on
8 of 11 references.
References · 8
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2020RetinaGAN: An Object-aware Approach to Sim-to-Real Transfercited by 68
- 2020End-to-end Autonomous Driving Perception with Sequential Latent Representation Learningcited by 11
- 2018Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Controlcited by 7
- 2020Guided Policy Search Model-based Reinforcement Learning for Urban Autonomous Drivingcited by 5
- 2020Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networkscited by 4
- 2020COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushingcited by 4
- 2017Cascade Attribute Learning Networkcited by 1
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Citing papers per year: 2024: 1.
1 paper.