# Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation

arXiv: https://arxiv.org/abs/2504.15876 · HTML: https://mlsanity.com/paper/2504.15876/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning](https://mlsanity.com/paper/2403.11552/) (2024)
- [Intelligent problem-solving as integrated hierarchical reinforcement learning](https://mlsanity.com/paper/2208.08731/) (2022)
- [Extended Tree Search for Robot Task and Motion Planning](https://mlsanity.com/paper/2103.05456/) (2021)
- [RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning](https://mlsanity.com/paper/2110.03420/) (2021)
- [Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control](https://mlsanity.com/paper/2210.01525/) (2022)
- [HiLMa-Res: A General Hierarchical Framework via Residual RL for Combining Quadrupedal Locomotion and Manipulation](https://mlsanity.com/paper/2407.06584/) (2024)
- [D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning](https://mlsanity.com/paper/2312.02731/) (2023)
- [Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty](https://mlsanity.com/paper/2406.07877/) (2024)

## Abstract (arXiv)

In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision-making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision-making into two layers, but their unidirectional structure fails to capture the interdependence between these layers, limiting adaptability in dynamic environments. Here, we propose a novel bidirectional approach based on hierarchical reinforcement learning, enabling dynamic interaction between the layers. This method effectively maps commands to task allocation and actions to path planning, while leveraging cross-training techniques to enhance learning across the hierarchical framework. Furthermore, we introduce a trajectory prediction model that bridges abstract task representations with actionable planning goals. In our experiments, it achieves over 80% in confrontation win rate and under 0.01 seconds in decision time, outperforming existing approaches. Demonstrations through large-scale tests and real-world robot experiments further emphasize the generalization capabilities and practical applicability of our method.