# Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference

arXiv: https://arxiv.org/abs/2205.08657 · HTML: https://mlsanity.com/paper/2205.08657/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 2 of 2)

- [ELFI: Engine for Likelihood-Free Inference](https://mlsanity.com/paper/1708.00707/) (2017)
- [Human-Robot Collaboration: From Psychology to Social Robotics](https://mlsanity.com/paper/1705.10146/) (2017)

## Built on by (most cited first, 5 of 5)

- [Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork](https://mlsanity.com/paper/2607.27177/) (2026)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)
- [Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration](https://mlsanity.com/paper/2404.03587/) (2024)
- [Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations](https://mlsanity.com/paper/2410.08681/) (2024)
- [Int3DNet: Scene-Motion Cross Attention Network for 3D Intention Prediction in Mixed Reality](https://mlsanity.com/paper/2603.13355/) (2026)

## Abstract (arXiv)

The combination of collaborative robots and end-to-end AI, promises flexible automation of human tasks in factories and warehouses. However, such promise seems a few breakthroughs away. In the meantime, humans and cobots will collaborate helping each other. For these collaborations to be effective and safe, robots need to model, predict and exploit human's intents for responsive decision making processes. Approximate Bayesian Computation (ABC) is an analysis-by-synthesis approach to perform probabilistic predictions upon uncertain quantities. ABC includes priors conveniently, leverages sampling algorithms for inference and is flexible to benefit from complex models, e.g. via simulators. However, ABC is known to be computationally too intensive to run at interactive frame rates required for effective human-robot collaboration tasks. In this paper, we formulate human reaching intent prediction as an ABC problem and describe two key performance innovations which allow computations at interactive rates. Our real-world experiments with a collaborative robot set-up, demonstrate the viability of our proposed approach. Experimental evaluations convey the advantages and value of human intent prediction for packing cooperative tasks. Qualitative results show how anticipating human's reaching intent improves human-robot collaboration without compromising safety. Quantitative task fluency metrics confirm the qualitative claims.