# Robust Robot Planning for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2302.13916 · HTML: https://mlsanity.com/paper/2302.13916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning](https://mlsanity.com/paper/1801.04099/) (2018)
- [Solving POMDPs by Searching the Space of Finite Policies](https://mlsanity.com/paper/1301.6720/) (2013)
- [Human-Robot Mutual Adaptation in Shared Autonomy](https://mlsanity.com/paper/1701.07851/) (2017)
- [Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations](https://mlsanity.com/paper/1802.06895/) (2018)
- [Solving infinite-horizon Dec-POMDPs using Finite State Controllers within JESP](https://mlsanity.com/paper/2109.08755/) (2021)
- [POMDP Model Learning for Human Robot Collaboration](https://mlsanity.com/paper/1803.11300/) (2018)

## Built on by (most cited first, 1 of 1)

- [ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration](https://mlsanity.com/paper/2402.19128/) (2024)

## Abstract (arXiv)

In human-robot collaboration, the objectives of the human are often unknown to the robot. Moreover, even assuming a known objective, the human behavior is also uncertain. In order to plan a robust robot behavior, a key preliminary question is then: How to derive realistic human behaviors given a known objective? A major issue is that such a human behavior should itself account for the robot behavior, otherwise collaboration cannot happen. In this paper, we rely on Markov decision models, representing the uncertainty over the human objective as a probability distribution over a finite set of objective functions (inducing a distribution over human behaviors). Based on this, we propose two contributions: 1) an approach to automatically generate an uncertain human behavior (a policy) for each given objective function while accounting for possible robot behaviors; and 2) a robot planning algorithm that is robust to the above-mentioned uncertainties and relies on solving a partially observable Markov decision process (POMDP) obtained by reasoning on a distribution over human behaviors. A co-working scenario allows conducting experiments and presenting qualitative and quantitative results to evaluate our approach.