# Human-robot collaboration and machine learning: a systematic review of recent research

arXiv: https://arxiv.org/abs/2110.07448 · HTML: https://mlsanity.com/paper/2110.07448/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 80 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Efficient Model Learning for Human-Robot Collaborative Tasks](https://mlsanity.com/paper/1405.6341/) (2014)
- [Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning](https://mlsanity.com/paper/1801.04099/) (2018)
- [Human-centered collaborative robots with deep reinforcement learning](https://mlsanity.com/paper/2007.01009/) (2020)
- [A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction](https://mlsanity.com/paper/1607.07939/) (2016)
- [Spiking Neural Networks for Early Prediction in Human Robot Collaboration](https://mlsanity.com/paper/1807.11096/) (2018)
- [A Nonparametric Motion Flow Model for Human Robot Cooperation](https://mlsanity.com/paper/1709.03211/) (2017)

## Built on by (most cited first, 20 of 20)

- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent](https://mlsanity.com/paper/2505.02024/) (2025)
- [A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors](https://mlsanity.com/paper/2302.13137/) (2023)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2308.02594/) (2023)
- [A roadmap for AI in robotics](https://mlsanity.com/paper/2507.19975/) (2025)
- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents](https://mlsanity.com/paper/2204.02889/) (2022)
- [Embodied AI: Emerging Risks and Opportunities for Policy Action](https://mlsanity.com/paper/2509.00117/) (2025)
- [XBG: End-to-end Imitation Learning for Autonomous Behaviour in Human-Robot Interaction and Collaboration](https://mlsanity.com/paper/2406.15833/) (2024)
- [Metarobotics for Industry and Society: Vision, Technologies, and Opportunities](https://mlsanity.com/paper/2404.00797/) (2024)
- [KnowPC: Knowledge-Driven Programmatic Reinforcement Learning for Zero-shot Coordination](https://mlsanity.com/paper/2408.04336/) (2024)
- [Target-Oriented Object Grasping via Multimodal Human Guidance](https://mlsanity.com/paper/2408.11138/) (2024)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [CASPER: Cognitive Architecture for Social Perception and Engagement in Robots](https://mlsanity.com/paper/2209.01012/) (2022)
- [Learning Multimodal Latent Dynamics for Human-Robot Interaction](https://mlsanity.com/paper/2311.16380/) (2023)
- [Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications](https://mlsanity.com/paper/2302.05763/) (2023)
- [Moving Out: Physically-grounded Human-AI Collaboration](https://mlsanity.com/paper/2507.18623/) (2025)
- [Active Visual Perception: Opportunities and Challenges](https://mlsanity.com/paper/2512.03687/) (2025)
- [RoboSubtaskNet: Temporal Sub-task Segmentation for Human-to-Robot Skill Transfer in Real-World Environments](https://mlsanity.com/paper/2602.10015/) (2026)

## Abstract (arXiv)

Technological progress increasingly envisions the use of robots interacting with people in everyday life. Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot, during the completion of a common objective, at the cognitive and physical level. In HRC works, a cognitive model is typically built, which collects inputs from the environment and from the user, elaborates and translates these into information that can be used by the robot itself. Machine learning is a recent approach to build the cognitive model and behavioural block, with high potential in HRC. Consequently, this paper proposes a thorough literature review of the use of machine learning techniques in the context of human-robot collaboration. 45 key papers were selected and analysed, and a clustering of works based on the type of collaborative tasks, evaluation metrics and cognitive variables modelled is proposed. Then, a deep analysis on different families of machine learning algorithms and their properties, along with the sensing modalities used, is carried out. Among the observations, it is outlined the importance of the machine learning algorithms to incorporate time dependencies. The salient features of these works are then cross-analysed to show trends in HRC and give guidelines for future works, comparing them with other aspects of HRC not appeared in the review.