# Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

arXiv: https://arxiv.org/abs/2202.07720 · HTML: https://mlsanity.com/paper/2202.07720/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Probabilistically Safe Robot Planning with Confidence-Based Human Predictions](https://mlsanity.com/paper/1806.00109/) (2018)
- [Interactive multi-modal motion planning with Branch Model Predictive Control](https://mlsanity.com/paper/2109.05128/) (2021)
- [Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models](https://mlsanity.com/paper/2109.14700/) (2021)
- [Cooperative Autonomous Vehicles that Sympathize with Human Drivers](https://mlsanity.com/paper/2107.00898/) (2021)
- [SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction](https://mlsanity.com/paper/2110.00843/) (2021)
- [Inference-Based Strategy Alignment for General-Sum Differential Games](https://mlsanity.com/paper/2002.04354/) (2020)
- [Dual Control for Approximate Bayesian Reinforcement Learning](https://mlsanity.com/paper/1510.03591/) (2015)
- [Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments](https://mlsanity.com/paper/2103.12517/) (2021)
- [Anytime Game-Theoretic Planning with Active Reasoning About Humans' Latent States for Human-Centered Robots](https://mlsanity.com/paper/2109.12490/) (2021)
- [Tractable Dual Optimal Stochastic Model Predictive Control: An Example in Healthcare](https://mlsanity.com/paper/1704.07770/) (2017)
- [Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty](https://mlsanity.com/paper/1912.10114/) (2019)

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

- [The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems](https://mlsanity.com/paper/2309.05837/) (2023)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Blending Data-Driven Priors in Dynamic Games](https://mlsanity.com/paper/2402.14174/) (2024)
- [Auto-Encoding Bayesian Inverse Games](https://mlsanity.com/paper/2402.08902/) (2024)
- [Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation](https://mlsanity.com/paper/2407.06056/) (2024)
- [Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace](https://mlsanity.com/paper/2304.01428/) (2023)
- [Active Constraint Learning in High Dimensions from Demonstrations](https://mlsanity.com/paper/2512.22757/) (2025)
- [Bayesian Inverse Games with High-Dimensional Multi-Modal Observations](https://mlsanity.com/paper/2601.00696/) (2026)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)

## Abstract (arXiv)

The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people's goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between robot trajectory optimization and human intent inference. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive human models with both continuous and categorical uncertainty. The efficacy of our approach is demonstrated with simulated driving examples.