# Robots Influencing Humans to Reveal their Goals during Collaboration and Competition

arXiv: https://arxiv.org/abs/2609.05519 · HTML: https://mlsanity.com/paper/2609.05519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 123 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- ["No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy](https://mlsanity.com/paper/2301.02555/) (2023)
- [Learning to Incentivize Other Learning Agents](https://mlsanity.com/paper/2006.06051/) (2020)
- [PDDLGym: Gym Environments from PDDL Problems](https://mlsanity.com/paper/2002.06432/) (2020)
- [Influencing Towards Stable Multi-Agent Interactions](https://mlsanity.com/paper/2110.08229/) (2021)
- [Model-Based Opponent Modeling](https://mlsanity.com/paper/2108.01843/) (2021)
- [Expected Value of Communication for Planning in Ad Hoc Teamwork](https://mlsanity.com/paper/2103.01171/) (2021)
- [Physical Interaction as Communication: Learning Robot Objectives Online from Human Corrections](https://mlsanity.com/paper/2107.02349/) (2021)
- [Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.01158/) (2023)
- [Human-Robot Mutual Adaptation in Shared Autonomy](https://mlsanity.com/paper/1701.07851/) (2017)
- [Active Goal Recognition](https://mlsanity.com/paper/1909.11173/) (2019)
- [Coordination with Humans via Strategy Matching](https://mlsanity.com/paper/2210.15099/) (2022)
- [ExpressEdit: Video Editing with Natural Language and Sketching](https://mlsanity.com/paper/2403.17693/) (2024)
- [Emergent Prosociality in Multi-Agent Games Through Gifting](https://mlsanity.com/paper/2105.06593/) (2021)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration](https://mlsanity.com/paper/1802.01780/) (2018)
- [I Know What You Meant: Learning Human Objectives by (Under)estimating Their Choice Set](https://mlsanity.com/paper/2011.06118/) (2020)
- [Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing](https://mlsanity.com/paper/2410.10062/) (2024)
- [Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2103.00683/) (2021)
- [Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication](https://mlsanity.com/paper/2405.14173/) (2024)
- [Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference](https://mlsanity.com/paper/2205.08657/) (2022)

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

- [Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration](https://mlsanity.com/paper/2609.13982/) (2026)

## Abstract (arXiv)

We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-conditioned policy divergence measure and incorporate them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain. We evaluate this approach in both a collaborative, fully observable cooking task and a competitive, partially observable hide-and-seek game, each in simulation and on real robots. In both scenarios, our method infers human goals more accurately and earlier than baseline strategies.