# Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/1802.01780 · HTML: https://mlsanity.com/paper/1802.01780/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling](https://mlsanity.com/paper/2502.15676/) (2025)
- [Dynamically Switching Human Prediction Models for Efficient Planning](https://mlsanity.com/paper/2103.07815/) (2021)
- [Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review](https://mlsanity.com/paper/2307.08024/) (2023)
- [Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review](https://mlsanity.com/paper/2505.07911/) (2025)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)
- [Learning a Visually Grounded Memory Assistant](https://mlsanity.com/paper/2210.03787/) (2022)
- [Intent-Driven LLM Ensemble Planning for Flexible Multi-Robot Disassembly: Demonstration on EV Batteries](https://mlsanity.com/paper/2510.17576/) (2025)
- [Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations](https://mlsanity.com/paper/2605.22986/) (2026)
- [Corrigible Assistance in One Round: Pragmatic-Pedagogic Best Response](https://mlsanity.com/paper/2607.27508/) (2026)

## Abstract (arXiv)

The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.