# TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning

arXiv: https://arxiv.org/abs/2412.02570 · HTML: https://mlsanity.com/paper/2412.02570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 3 Dec 2024.

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

- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [A survey of the Schr"odinger problem and some of its connections with optimal transport](https://mlsanity.com/paper/1308.0215/) (2013)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraints](https://mlsanity.com/paper/1807.03223/) (2018)
- [Robust Markov Decision Processes without Model Estimation](https://mlsanity.com/paper/2302.01248/) (2023)
- [Exploration-Exploitation Trade-off in Reinforcement Learning on Online Markov Decision Processes with Global Concave Rewards](https://mlsanity.com/paper/1905.06466/) (2019)
- [Decentralized MCTS via Learned Teammate Models](https://mlsanity.com/paper/2003.08727/) (2020)
- [Multi-Robot Coordination and Planning in Uncertain and Adversarial Environments](https://mlsanity.com/paper/2105.00389/) (2021)
- [Efficient Dependency-Guided Named Entity Recognition](https://mlsanity.com/paper/1810.08436/) (2018)
- [Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment](https://mlsanity.com/paper/2312.03263/) (2023)
- [ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates](https://mlsanity.com/paper/2403.01564/) (2024)

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

- [TRACE: A Self-Improving Framework for Robot Behavior Forecasting with Vision-Language Models](https://mlsanity.com/paper/2503.00761/) (2025)
- [Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements](https://mlsanity.com/paper/2505.13837/) (2025)

## Abstract (arXiv)

Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinations within time constraints, the exact policies these adversaries will employ remain unknown. Traditional approaches address this challenge by treating the adversary's state as a partially observable element, leading to a formulation as a Partially Observable Markov Decision Process (POMDP). However, the induced belief-space dynamics in a POMDP require knowledge of the system's transition dynamics, which, in this case, depend on the adversary's unknown policy. Our key observation is that while an adversary's exact policy is unknown, their behavior is necessarily constrained by their mission objectives and the physical environment, allowing us to characterize the space of possible behaviors without assuming specific policies. In this paper, we develop Task-Aware Behavior Fields (TAB-Fields), a representation that captures adversary state distributions over time by computing the most unbiased probability distribution consistent with known constraints. We construct TAB-Fields by solving a constrained optimization problem that minimizes additional assumptions about adversary behavior beyond mission and environmental requirements. We integrate TAB-Fields with standard planning algorithms by introducing TAB-conditioned POMCP, an adaptation of Partially Observable Monte Carlo Planning. Through experiments in simulation with underwater robots and hardware implementations with ground robots, we demonstrate that our approach achieves superior performance compared to baselines that either assume specific adversary policies or neglect mission constraints altogether. Evaluation videos and code are available at https://tab-fields.github.io.