# Human-centered collaborative robots with deep reinforcement learning

arXiv: https://arxiv.org/abs/2007.01009 · HTML: https://mlsanity.com/paper/2007.01009/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

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

- [Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets](https://mlsanity.com/paper/2109.13396/) (2021)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Human-robot collaboration and machine learning: a systematic review of recent research](https://mlsanity.com/paper/2110.07448/) (2021)
- [A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors](https://mlsanity.com/paper/2302.13137/) (2023)
- [A Survey of Reinforcement Learning for Optimization in Automation](https://mlsanity.com/paper/2502.09417/) (2025)
- [Deep reinforcement learning applied to an assembly sequence planning problem with user preferences](https://mlsanity.com/paper/2304.06567/) (2023)
- [Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations](https://mlsanity.com/paper/2210.01645/) (2022)
- [Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications](https://mlsanity.com/paper/2302.05763/) (2023)
- [CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems](https://mlsanity.com/paper/2311.04562/) (2023)
- [Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot Collaboration](https://mlsanity.com/paper/2404.19253/) (2024)
- [Enhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay](https://mlsanity.com/paper/2512.05320/) (2025)
- [Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions](https://mlsanity.com/paper/2602.07341/) (2026)

## Abstract (arXiv)

We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent to complete the task. The framework is learned end-to-end in an unsupervised fashion addressing the perception uncertainties and decision making in an integrated manner. The framework is shown to provide more fluent coordination between human and robot partners on an example task of packaging compared to alternatives for which perception and decision-making systems are learned independently, using supervised learning. The foremost benefit of the proposed approach is that it allows for fast adaptation to new human partners and tasks since tedious annotation of motion data is avoided and the learning is performed on-line.