# A survey of robot learning from demonstrations for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/1710.08789 · HTML: https://mlsanity.com/paper/1710.08789/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 30 of 38)

- [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection](https://mlsanity.com/paper/2206.06291/) (2022)
- [Model-Based Runtime Monitoring with Interactive Imitation Learning](https://mlsanity.com/paper/2310.17552/) (2023)
- [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023)
- [Learning and Adapting Agile Locomotion Skills by Transferring Experience](https://mlsanity.com/paper/2304.09834/) (2023)
- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [A survey and taxonomy of loss functions in machine learning](https://mlsanity.com/paper/2301.05579/) (2023)
- [A Survey of Demonstration Learning](https://mlsanity.com/paper/2303.11191/) (2023)
- [Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization](https://mlsanity.com/paper/2110.04686/) (2021)
- [Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation](https://mlsanity.com/paper/2402.07127/) (2024)
- [Quick on the Uptake: Eliciting Implicit Intents from Human Demonstrations for Personalized Mobile-Use Agents](https://mlsanity.com/paper/2508.08645/) (2025)
- [A Geometric Perspective on Visual Imitation Learning](https://mlsanity.com/paper/2003.02768/) (2020)
- [EC^2: Emergent Communication for Embodied Control](https://mlsanity.com/paper/2304.09448/) (2023)
- [PEAR: Phrase-Based Hand-Object Interaction Anticipation](https://mlsanity.com/paper/2407.21510/) (2024)
- [From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning](https://mlsanity.com/paper/2603.10263/) (2026)
- [Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation](https://mlsanity.com/paper/2211.03393/) (2022)
- [A Comparison of Imitation Learning Algorithms for Bimanual Manipulation](https://mlsanity.com/paper/2408.06536/) (2024)
- [Efficiently Guiding Imitation Learning Agents with Human Gaze](https://mlsanity.com/paper/2002.12500/) (2020)
- [Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations](https://mlsanity.com/paper/2112.04612/) (2021)
- [Teaching Robots to Span the Space of Functional Expressive Motion](https://mlsanity.com/paper/2203.02091/) (2022)
- [Consistency Matters: Defining Demonstration Data Quality Metrics in Robot Learning from Demonstration](https://mlsanity.com/paper/2412.14309/) (2024)
- [SAFARI: Safe and Active Robot Imitation Learning with Imagination](https://mlsanity.com/paper/2011.09586/) (2020)
- [MMP++: Motion Manifold Primitives with Parametric Curve Models](https://mlsanity.com/paper/2310.17072/) (2023)
- [Defining a New NLP Playground](https://mlsanity.com/paper/2310.20633/) (2023)
- [Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning](https://mlsanity.com/paper/2211.15339/) (2022)
- [Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2310.20650/) (2023)
- [DIP-RL: Demonstration-Inferred Preference Learning in Minecraft](https://mlsanity.com/paper/2307.12158/) (2023)
- [What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2309.07808/) (2023)

## Abstract (arXiv)

Robot learning from demonstration (LfD) is a research paradigm that can play an important role in addressing the issue of scaling up robot learning. Since this type of approach enables non-robotics experts can teach robots new knowledge without any professional background of mechanical engineering or computer programming skills, robots can appear in the real world even if it does not have any prior knowledge for any tasks like a new born baby. There is a growing body of literature that employ LfD approach for training robots. In this paper, I present a survey of recent research in this area while focusing on studies for human-robot collaborative tasks. Since there are different aspects between stand-alone tasks and collaborative tasks, researchers should consider these differences to design collaborative robots for more effective and natural human-robot collaboration (HRC). In this regard, many researchers have shown an increased interest in to make better communication framework between robots and humans because communication is a key issue to apply LfD paradigm for human-robot collaboration. I thus review some recent works that focus on designing better communication channels/methods at the first, then deal with another interesting research method, Interactive/Active learning, after that I finally present other recent approaches tackle a more challenging problem, learning of complex tasks, in the last of the paper.