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2025

[J35] NL2HLTL2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Specifications
Shaojun Xu, Xusheng Luo, Yutong Huang, Letian Leng, Ruixuan Liu and Changliu Liu
IEEE Robotics and Automation Letters, 2025
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To enable non-experts to specify long-horizon, multi-robot collaborative tasks, language models are increasingly used to translate natural language commands into formal specifications. However, because translation can occur in multiple ways, such translations may lack accuracy or lead to inefficient multi-robot planning. Our key insight is that concise hierarchical specifications can simplify planning while remaining straightforward to derive from human instructions. We propose Nl2Hltl2Plan, a framework that translates natural language commands into hierarchical Linear Temporal Logic (LTL) and solves the corresponding planning problem. The translation involves two steps leveraging Large Language Models (LLMs). First, an LLM transforms instructions into a Hierarchical Task Tree, capturing logical and temporal relations. Next, a fine-tuned LLM converts sub-tasks into flat LTL formulas, which are aggregated into hierarchical specifications, with the lowest level corresponding to ordered robot actions. These specifications are then used with off-the-shelf planners. Our Nl2Hltl2Plan demonstrates the potential of LLMs in hierarchical reasoning for multi-robot task planning. Evaluations in simulation and real-world experiments with human participants show that Nl2Hltl2Plan outperforms existing methods, handling more complex instructions while achieving higher success rates and lower costs in task allocation and planning.

2024

[J25] Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Xusheng Luo, Shaojun Xu, Ruixuan Liu and Changliu Liu
IEEE Robotics and Automation Letters, 2024
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Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. A recent development has been the hierarchical representation of LTL (Luo et al., 2024) that contains multiple temporal logic specifications, providing a more interpretable framework. However, the proposed planning algorithm assumes the independence of robots within each specification, limiting their application to multi-robot coordination with complex temporal constraints. In this work, we formulated a decomposition-based hierarchical framework. At the high level, each specification is first decomposed into a set of atomic sub-tasks. We further infer the temporal relations among the sub-tasks of different specifications to construct a task network. Subsequently, a Mixed Integer Linear Program is used to assign sub-tasks to various robots. At the lower level, domain-specific controllers are employed to execute sub-tasks. Our approach was experimentally applied to domains of navigation and manipulation. The simulation demonstrated that our approach can find better solutions using less runtimes.

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[C76] A Lightweight and Transferable Design for Robust LEGO Manipulation
Ruixuan Liu, Yifan Sun and Changliu Liu
International Symposium of Flexible Automation, 2024
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2023

[C63] Zero-shot Transferable and Persistently Feasible Safe Control for High Dimensional Systems by Consistent Abstraction
Tianhao Wei, Shucheng Kang, Ruixuan Liu and Changliu Liu
IEEE Conference on Decision and Control, 2023
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[W] Robotic LEGO Assembly and Disassembly from Human Demonstration
Ruixuan Liu, Yifan Sun and Changliu Liu
ACC Workshop on Recent Advancement of Human Autonomy Interaction and Integration, 2023
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[U] Simulation-aided Learning from Demonstration for Robotic LEGO Construction
Ruixuan Liu, Alan Chen, Xusheng Luo and Changliu Liu
arXiv:2309.11010, 2023
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2022

[C50] Task-agnostic Adaptation for Safe Human-robot Handover
Ruixuan Liu, Rui Chen and Changliu Liu
IFAC Workshop on Cyber-Physical Human Systems, 2022
Best Student Paper Award
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[C44] Jerk-bounded Position Controller with Real-Time Task Modification for Interactive Industrial Robots
Ruixuan Liu, Rui Chen, Yifan Sun, Yu Zhao and Changliu Liu
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2022
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Industrial robots are widely used in many applications with structured and deterministic environments. However, the contemporary need requires industrial robots to intelligently operate in dynamic environments. It is challenging to design a safe and efficient robotic system with industrial robots in a dynamic environment for several reasons. First, most industrial robots require the input to have specific formats, which takes additional efforts to convert from task-level user commands. Second, existing robot drivers do not support overwriting ongoing tasks in real-time, which hinders the robot from responding to the dynamic environment. Third, most industrial robots only expose motion-level control, making it challenging to enforce dynamic constraints during trajectory tracking. To resolve the above challenges, this paper presents a jerk-bounded position control driver (JPC) for industrial robots. JPC provides a unified interface for tracking complex trajectories and is able to enforce dynamic constraints using motion-level control, without accessing servo-level control. Most importantly, JPC enables real-time trajectory modification. Users can overwrite the ongoing task with a new one without violating dynamic constraints. The proposed JPC is implemented and tested on the FANUC LR Mate 200id/7L robot with both artificially generated data and an interactive robot handover task. Experiments show that the proposed JPC can track complex trajectories accurately within dynamic limits and seamlessly switch to new trajectory references before the ongoing task ends.

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[C42] Safe Interactive Industrial Robots using Jerk-based Safe Set Algorithm
Ruixuan Liu, Rui Chen and Changliu Liu
International Symposium on Flexible Automation, 2022
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The need to increase the flexibility of production lines is calling for robots to collaborate with human workers. However, existing interactive industrial robots only guarantee intrinsic safety (reduce collision impact), but not interactive safety (collision avoidance), which greatly limited their flexibility. The issue arises from two limitations in existing control software for industrial robots: 1) lack of support for real-time trajectory modification; 2) lack of intelligent safe control algorithms with guaranteed collision avoidance under robot dynamics constraints. To address the first issue, a jerk-bounded position controller (JPC) was developed previously. This paper addresses the second limitation, on top of the JPC. Specifically, we introduce a jerk-based safe set algorithm (JSSA) to ensure collision avoidance while considering the robot dynamics constraints. The JSSA greatly extends the scope of the original safe set algorithm, which has only been applied for second-order systems with unbounded accelerations. The JSSA is implemented on the FANUC LR Mate 200id/7L robot and validated with HRI tasks. Experiments show that the JSSA can consistently keep the robot at a safe distance from the human while executing the designated task.

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