Author name cluster

Minglong Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

15 papers

2 author rows

AAAI Conference 2026 Conference Paper

CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation

Yishuai Cai
Xinglin Chen
Yunxin Mao
Kun Hu
Minglong Li

Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automated generation of reliable BTs. However, BT planning typically assumes that a well-designed BT system is already grounded—comprising high-level action models and low-level control policies—which often requires extensive expert knowledge and manual effort. In this paper, we formalize the BT Grounding problem: the automated construction of a complete and consistent BT system. We analyze its complexity and introduce CABTO (Context-Aware Behavior Tree grOunding), the first framework to efficiently solve this challenge. CABTO leverages pre-trained Large Models (LMs) to heuristically search the space of action models and control policies, guided by contextual feedback from BT planners and environmental observations. Experiments spanning seven task sets across three distinct robotic manipulation scenarios demonstrate CABTO’s effectiveness and efficiency in generating complete and consistent behavior tree systems.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

BTPG: A Platform and Benchmark for Behavior Tree Planning in Everyday Service Robots

Xinglin Chen
Yishuai Cai
Minglong Li
Yunxin Mao
Zhou Yang
Wenjing Yang
Weixia Xu
Ji Wang

Behavior Trees (BTs) are a widely used control architecture in robotics, renowned for their robustness and safety, which are especially crucial for everyday service robots. Recently, several methods have been proposed to automatically plan BTs to accomplish specific tasks. However, existing research in BT planning lacks two main aspects: (1) the absence of a standard platform for modeling and planning BTs, along with testing benchmarks; and (2) insufficient metrics for a comprehensive evaluation of BT planning algorithms. In this paper, we propose Behavior Tree Planning Gym (BTPG), the first platform and benchmark for BT planning in everyday service robots. In BTPG, behavior nodes are represented by predicate logic, and objects are categorized to better define the predicate domains and action models. The BT planning problem is then formulated in the STRIPS style. We support four environments and three simulators with different action models, which cover most of the needs of everyday service activities. We design a dataset generator for each environment and test three state-of-the-art BT planning algorithms, as well as one proposed by us, using various common metrics. In addition, we design three advanced metrics, planning progress, region distance, and execution robustness, to gain deeper insights into these BT planning algorithms. With a standard test benchmark, we hope BTPG can inspire and accelerate progress in the field of BT planning. Our codes are available at https: //github. com/DIDS-EI/BTPG.

PDF Details DOI

ICRA Conference 2025 Conference Paper

FutureNet-LoF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding

Mingkun Wang
Xiaoguang Ren
Ruochun Jin
Minglong Li
Xiaochuan Zhang
Changqian Yu
Mingxu Wang
Wenjing Yang 0002

Most prior motion prediction endeavors in autonomous driving have inadequately encoded future scenarios, leading to predictions that may fail to accurately capture the diverse movements of agents (e. g. , vehicles or pedestrians). To address this, we propose FutureNet, which explicitly integrates initially predicted trajectories into the future scenario and further encodes these future contexts to enhance subsequent forecasting. Additionally, most previous motion forecasting works have focused on predicting independent futures for each agent. However, safe and smooth autonomous driving requires accurately predicting the diverse future behaviors of numerous surrounding agents jointly in complex dynamic environments. Given that all agents occupy certain potential travel spaces and possess lane driving priority, we propose Lane Occupancy Field (LOF), a new representation with lane semantics for motion forecasting in autonomous driving. LOF can simultaneously capture the joint probability distribution of all road participants' future spatial-temporal positions. Due to the high compatibility between lane occupancy field prediction and trajectory prediction, we propose a novel network for joint prediction of these two tasks. Our approach ranks 1st on two large-scale motion forecasting benchmarks: Argoverse 1 and Argoverse 2, while it is also the champion method of the CVPR 2024 Argoverse 2 motion forecasting challenge.

Details

ICRA Conference 2025 Conference Paper

HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning

Yishuai Cai
Xinglin Chen
Yunxin Mao
Minglong Li
Shaowu Yang
Wenjing Yang 0002
Ji Wang 0001

Behavior Trees (BTs) are increasingly becoming a popular control structure in robotics due to their modularity, reactivity, and robustness. In terms of BT generation methods, BT planning shows promise for generating reliable BTs. However, the scalability of BT planning is often constrained by prolonged planning times in complex scenarios, largely due to a lack of domain knowledge. In contrast, pre-trained Large Language Models (LLMs) have demonstrated task reasoning capabilities across various domains, though the correctness and safety of their planning remain uncertain. This paper proposes integrating BT planning with LLM reasoning, introducing Heuristic Behavior Tree Planning (HBTP)-a reliable and efficient framework for BT generation. The key idea in HBTP is to leverage LLMs for task-specific reasoning to generate a heuristic path, which BT planning can then follow to expand efficiently. We first introduce the heuristic BT expansion process, along with two heuristic variants designed for optimal planning and satisficing planning, respectively. Then, we propose methods to address the inaccuracies of LLM reasoning, including action space pruning and reflective feedback, to further enhance both reasoning accuracy and planning efficiency. Experiments demonstrate the theoretical bounds of HBTP, and results from four datasets confirm its practical effectiveness in everyday service robot applications.

Details

AAAI Conference 2025 Conference Paper

MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration

Yishuai Cai
Xinglin Chen
Zhongxuan Cai
Yunxin Mao
Minglong Li
Wenjing Yang
Ji Wang

Multi-robot task planning and collaboration are critical challenges in robotics. While Behavior Trees (BTs) have been established as a popular control architecture and are plannable for a single robot, the development of effective multi-robot BT planning algorithms remains challenging due to the complexity of coordinating diverse action spaces. We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness. MRBTP features cross-tree expansion to coordinate heterogeneous actions across different BTs to achieve the team's goal. For homogeneous actions, we retain backup structures among BTs to ensure robustness and prevent redundant execution through intention sharing. While MRBTP is capable of generating BTs for both homogeneous and heterogeneous robot teams, its efficiency can be further improved. We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot. These relevant actions can be pre-planned to form long-horizon subtrees, significantly enhancing the planning speed and collaboration efficiency of MRBTP. We evaluate our algorithm in warehouse management and everyday service scenarios. Results demonstrate MRBTP's robustness and execution efficiency under varying settings, as well as the ability of the pre-trained LLM to generate effective task-specific subtrees for MRBTP.

PDF Details DOI

AAMAS Conference 2025 Conference Paper

PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning

Kun Hu
Muning Wen
Xihuai Wang
Shao Zhang
Yiwei Shi
Minne Li
Minglong Li
Ying Wen

Multi-Agent Reinforcement Learning (MARL) faces challenges in coordinating agents due to complex interdependencies within multiagent systems. Most MARL algorithms use the simultaneous decisionmaking paradigm but ignore the action-level dependencies among agents, which reduces coordination efficiency. In contrast, the sequential decision-making paradigm provides finer-grained supervision for agent decision order, presenting the potential for handling dependencies via better decision order management. However, determining the optimal decision order remains a challenge. In this paper, we introduce Action Generation with Plackett-Luce Sampling (AGPS), a novel mechanism for agent decision order optimization. We model the order determination task as a Plackett-Luce sampling process to address issues such as ranking instability and vanishing gradient during the network training process. AGPS realizes credit-based decision order determination by establishing a bridge between the significance of agents’ local observations and their decision credits, thus facilitating order optimization and dependency management. Integrating AGPS with the Multi-Agent Transformer, we propose the Prioritized Multi-Agent Transformer (PMAT), a sequential decision-making MARL algorithm with decision order optimization. Experiments on benchmarks including StarCraft Multi-Agent Challenge, Google Research Football, and Multi-Agent MuJoCo show that PMAT outperforms state-ofthe-art algorithms, greatly enhancing coordination efficiency. ∗Correspondence to Minglong Li and Ying Wen. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Y. Vorobeychik, S. Das, A. Nowé (eds.), May 19 – 23, 2025, Detroit, Michigan, USA. © 2025 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org).

PDF

IROS Conference 2024 Conference Paper

Coalition Formation Game Approach for Task Allocation in Heterogeneous Multi-Robot Systems under Resource Constraints

Liwang Zhang
Dong Liang
Minglong Li
Wenjing Yang 0002
Shaowu Yang

This paper studies a case of the multi-robot task allocation (MRTA) problem, where each unmanned aerial vehicle (UAV) is endowed with multiple but limited resources. Completing each task necessitates UAVs to combine different resources through coalition formation, which will incur various costs including flight cost, execution cost, and cooperation cost. To minimize the total cost while maximizing both task completion rate and resource utilization rate, we model the MRTA problem of the UAVs as a leader-follower coalition formation game. In this game, leader UAVs coordinate follower UAVs to fulfill task resource requisites. Meanwhile, follower UAVs select suitable coalitions to join based on the altruistic preference. Theoretical analysis confirms the existence of a Nash stable partition in the coalition formation game. To achieve this stable partition, we propose a coalition formation algorithm. Simulation experiments validate that the proposed algorithm outperforms existing methods for the MRTA problem under resource constraints in terms of both task completion rate and resource utilization rate.

Details

IJCAI Conference 2024 Conference Paper

Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions

Xinglin Chen
Yishuai Cai
Yunxin Mao
Minglong Li
Wenjing Yang
Weixia Xu
Ji Wang

Robots executing tasks following human instructions in domestic or industrial environments essentially require both adaptability and reliability. Behavior Tree (BT) emerges as an appropriate control architecture for these scenarios due to its modularity and reactivity. Existing BT generation methods, however, either do not involve interpreting natural language or cannot theoretically guarantee the BTs' success. This paper proposes a two-stage framework for BT generation, which first employs large language models (LLMs) to interpret goals from high-level instructions, then constructs an efficient goal-specific BT through the Optimal Behavior Tree Expansion Algorithm (OBTEA). We represent goals as well-formed formulas in first-order logic, effectively bridging intent understanding and optimal behavior planning. Experiments in the service robot validate the proficiency of LLMs in producing grammatically correct and accurately interpreted goals, demonstrate OBTEA's superiority over the baseline BT Expansion algorithm in various metrics, and finally confirm the practical deployability of our framework. The project website is https: //dids-ei. github. io/Project/LLM-OBTEA.

PDF Details DOI

ICRA Conference 2024 Conference Paper

Task Allocation in Heterogeneous Multi-Robot Systems Based on Preference-Driven Hedonic Game

Liwang Zhang
Minglong Li
Wenjing Yang 0002
Shaowu Yang

Multiple preferences between robots and tasks have been largely overlooked in previous research on Multi-Robot Task Allocation (MRTA) problems. In this paper, we propose a preference-driven approach based on hedonic game to address the task allocation problem of muti-robot systems in emergency rescue scenarios. We present a distributed framework considering various preferences between robots and tasks to determine the division of coalitions in such problems and evaluate the scalability and adaptability of our algorithm through relevant experiments. Furthermore, considering the strict communication limitations in emergency rescue scenarios, we have verified that our algorithm can efficiently converge to a Nash-stable coalition partition even in conditions of insufficient communication distance.

Details

IROS Conference 2023 Conference Paper

Evolving Physical Instinct for Morphology and Control Co-Adaption

Xinglin Chen
Da Huang
Minglong Li
Yishuai Cai
Zhuoer Wen
Zhongxuan Cai
Wenjing Yang 0002

The capability of a robot to perform tasks depends not only on precise motion control, but also on a well-suited body morphology. Adapting both morphology and control of robots to improve their task performance has been a widely studied and long-standing issue. While the bio-inspired bi-level optimization framework has gained popularity in recent years, it suffers from high computation complexity due to the time-consuming and inefficient learning process for each morphology. In fact, in nature, besides the adaptive morphology and the intelligent brain, animals also possess an important gift, which is physical instinct. These instincts allow animals to respond quickly to their surroundings in the neonatal period, facilitating skills acquisition. Inspired by this, we propose an evolvable instinct controller to enhance the morphology-control co-adaption. The instinct controller suggests rough motion inclinations, which require minimal domain knowledge and entail less sophisticated design. Its purpose is to assist the main controller in learning fine-grained and robust control efficiently. We implemented this idea in the context of legged locomotion and designed the instinct controller using phase-based FSMs. We propose the instinct-based co-adaption algorithm and construct GPU parallel simulation experiments on different morphology prototypes. The results indicate that combining the co-adaption process with instinct evolution leads to the development of superior morphologies and robust controllers compared with the conventional co-adaption approach, with minimal additional time cost.

Details

ICRA Conference 2023 Conference Paper

Memory-based Exploration-value Evaluation Model for Visual Navigation

Yongquan Feng
Liyang Xu
Minglong Li
Ruochun Jin
Da Huang
Shaowu Yang
Wenjing Yang 0002

We propose a hierarchical visual navigation solution, called Memory-based Exploration-value Evaluation Model (MEEM), to improve the agent's navigation performance. MEEM employs a hierarchical policy to tackle the challenge of sparse rewards, holds an episodic memory to store the historical information of the agent, and applies an Exploration-value Evaluation Model to calculate an exploration-value for action planning at each location in the observable area. We experimentally verify MEEM by navigation performance comparison on two datasets including the grid-map dataset and the 3D scenes Gibson dataset, where our approach achieves state-of-the-art performance on both. Specifically, the overall success rate of MEEM is 95% on the grid-map dataset while the best competitor reaches 68% only. As for the Gibson dataset, the success rate of ours and the best competitor SemExp are 69. 8% and 54. 4%, respectively. Ablation analysis on the tile-map dataset indicates that all three components of MEEM have positive effects.

Details

IROS Conference 2023 Conference Paper

Task2Morph: Differentiable Task-Inspired Framework for Contact-Aware Robot Design

Yishuai Cai
Shaowu Yang
Minglong Li
Xinglin Chen
Yunxin Mao
Xiaodong Yi 0002
Wenjing Yang 0002

Optimizing the morphologies and the controllers that adapt to various tasks is a critical issue in the field of robot design, aka. embodied intelligence. Previous works typically model it as a joint optimization problem and use search-based methods to find the optimal solution in the morphology space. However, they ignore the implicit knowledge of task-to-morphology mapping which can directly inspire robot design. For example, flipping heavier boxes tends to require more muscular robot arms. This paper proposes a novel and general differentiable task-inspired framework for contact-aware robot design called Task2Morph. We abstract task features highly related to task performance and use them to build a task-to-morphology mapping. Further, we embed the mapping into a differentiable robot design process, where the gradient information is leveraged for both the mapping learning and the whole optimization. The experiments are conducted on three scenarios, and the results validate that Task2Morph outperforms DiffHand, which lacks a task-inspired morphology module, in terms of efficiency and effectiveness.

Details

AAAI Conference 2021 Conference Paper

BT Expansion: a Sound and Complete Algorithm for Behavior Planning of Intelligent Robots with Behavior Trees

Zhongxuan Cai
Minglong Li
Wanrong Huang
Wenjing Yang

Behavior Trees (BTs) have attracted much attention in the robotics field in recent years, which generalize existing control architectures and bring unique advantages for building robot systems. Automated synthesis of BTs can reduce human workload and build behavior models for complex tasks beyond the ability of human design, but theoretical studies are almost missing in existing methods because it is difficult to conduct formal analysis with the classic BT representations. As a result, they may fail in tasks that are actually solvable. This paper proposes BT expansion, an automated planning approach to building intelligent robot behaviors with BTs, and proves the soundness and completeness through the statespace formulation of BTs. The advantages of blended reactive planning and acting are formally discussed through the region of attraction of BTs, by which robots with BT expansion are robust to any resolvable external disturbances. Experiments with a mobile manipulator and test sets are simulated to validate the effectiveness and efficiency, where the proposed algorithm surpasses the baseline by virtue of its soundness and completeness. To the best of our knowledge, it is the first time to leverage the state-space formulation to synthesize BTs with a complete theoretical basis.

PDF Details

AAAI Conference 2021 Conference Paper

Dec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination

Minglong Li
Zhongxuan Cai
Wenjing Yang
Lixia Wu
Yinghui Xu
Ji Wang

Multi-agent coordination tends to benefit from efficient communication, where cooperation often happens based on exchanging information about what the agents intend to do, i. e. intention sharing. It becomes a key problem to model the intention by some proper abstraction. Currently, it is either too coarse such as final goals or too fined as primitive steps, which is inefficient due to the lack of modularity and semantics. In this paper, we design a novel multi-agent coordination protocol based on subgoal intentions, defined as the probability distribution over feasible subgoal sequences. The subgoal intentions encode macro-action behaviors with modularity so as to facilitate joint decision making at higher abstraction. Built over the proposed protocol, we present Dec-SGTS (Decentralized Sub-Goal Tree Search) to solve decentralized online multi-agent planning hierarchically and efficiently. Each agent runs Dec-SGTS asynchronously by iteratively performing three phases including local sub-goal tree search, local subgoal intention update and global subgoal intention sharing. We conduct the experiments on courier dispatching problem, and the results show that Dec-SGTS achieves much better reward while enjoying a significant reduction of planning time and communication cost compared with Dec-MCTS (Decentralized Monte Carlo Tree Search).

PDF Details

IJCAI Conference 2019 Conference Paper

Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty

Minglong Li
Wenjing Yang
Zhongxuan Cai
Shaowu Yang
Ji Wang

The performance of decentralized multi-agent systems tends to benefit from information sharing and its effective utilization. However, too much or unnecessary sharing may hinder the performance due to the delay, instability and additional overhead of communications. Aiming to a satisfiable coordination performance, one would prefer the cost of communications as less as possible. In this paper, we propose an approach for improving the sharing utilization by integrating information sharing with prediction in decentralized planning. We present a novel planning algorithm by combining decision sharing and prediction based on decentralized Monte Carlo Tree Search called Dec-MCTS-SP. Each agent grows a search tree guided by the rewards calculated by the joint actions, which can not only be sampled from the shared probability distributions over action sequences, but also be predicted by a sufficiently-accurate and computationally-cheap heuristics-based method. Besides, several policies including sparse and discounted UCT and DIY-bonus are leveraged for performance improvement. We have implemented Dec-MCTS-SP in the case study on multi-agent information gathering under threat and uncertainty, which is formulated as Decentralized Partially Observable Markov Decision Process (Dec-POMDP). The factored belief vectors are integrated into Dec-MCTS-SP to handle the uncertainty. Comparing with the random, auction-based algorithm and Dec-MCTS, the evaluation shows that Dec-MCTS-SP can reduce communication cost significantly while still achieving a surprisingly higher coordination performance.

PDF Details