# Dynamically Switching Human Prediction Models for Efficient Planning

arXiv: https://arxiv.org/abs/2103.07815 · HTML: https://mlsanity.com/paper/2103.07815/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 13 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Pragmatic-Pedagogic Value Alignment](https://mlsanity.com/paper/1707.06354/) (2017)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://mlsanity.com/paper/1805.07687/) (2018)
- [LESS is More: Rethinking Probabilistic Models of Human Behavior](https://mlsanity.com/paper/2001.04465/) (2020)
- [The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving](https://mlsanity.com/paper/1702.00858/) (2017)
- [Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration](https://mlsanity.com/paper/1802.01780/) (2018)
- [Agent Prioritization for Autonomous Navigation](https://mlsanity.com/paper/1909.08792/) (2019)

## Built on by (most cited first, 2 of 2)

- [Optimal Cost Design for Model Predictive Control](https://mlsanity.com/paper/2104.11353/) (2021)
- [Does Unpredictability Influence Driving Behavior?](https://mlsanity.com/paper/2307.15287/) (2023)

## Abstract (arXiv)

As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously; a more complex one might predict that the human will act optimally, disregarding the robot; whereas an even more complex one might capture the robot's ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.