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Yuqian Jiang

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10 papers

2 author rows

IROS Conference 2025 Conference Paper

L3M+P: Lifelong Planning with Large Language Models

Krish Agarwal
Yuqian Jiang
Jiaheng Hu
Bo Liu 0042
Peter Stone 0001

By combining classical planning methods with large language models (LLMs), recent research such as LLM+P has enabled agents to plan for general tasks given in natural language. However, scaling these methods to general-purpose service robots remains challenging: (1) classical planning algorithms generally require a detailed and consistent specification of the environment, which is not always readily available; and (2) existing frameworks mainly focus on isolated planning tasks, whereas robots are often meant to serve in long-term continuous deployments, and therefore must maintain a dynamic memory of the environment which can be updated with multi-modal inputs and extracted as planning knowledge for future tasks. To address these two issues, this paper introduces L3M+P (Lifelong LLM+P), a framework that uses an external knowledge graph as a representation of the world state. The graph can be updated from multiple sources of information, including sensory input and natural language interactions with humans. L3M+P enforces rules for the expected format of the absolute world state graph to maintain consistency between graph updates. At planning time, given a natural language description of a task, L3M+P retrieves context from the knowledge graph and generates a problem definition for classical planners. Evaluated on household robot simulators and on a real-world service robot, L3M+P achieves significant improvement over baseline methods both on accurately registering natural language state changes and on correctly generating plans, thanks to the knowledge graph retrieval and verification.

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IROS Conference 2023 Conference Paper

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning

Xiaohan Zhang 0002
Yifeng Zhu
Yan Ding 0002
Yuqian Jiang
Yuke Zhu
Peter Stone 0001
Shiqi Zhang 0001

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A well-developed state space enables the desirable distribution of limited computational resources between task planning and motion planning. However, developing such task-level state spaces can be non-trivial in practice. In this paper, we consider a long horizon mobile manipulation domain including repeated navigation and manipulation. We propose Symbolic State Space Optimization (S3O) for computing a set of abstracted locations and their 2D geometric groundings for generating task-motion plans in such domains. Our approach has been extensively evaluated in simulation and demonstrated on a real mobile manipulator working on clearing up dining tables. Results show the superiority of the proposed method over TAMP baselines in task completion rate and execution time.

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IJCAI Conference 2021 Conference Paper

Planning and Reinforcement Learning for General-Purpose Service Robots

Yuqian Jiang

Despite recent progress in AI and robotics research, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This Ph. D. research aims to combine symbolic planning and reinforcement learning to reason about high-level robot tasks and adapt to the real world. We will introduce task planning algorithms that adapt to the environment and other agents, as well as reinforcement learning methods that are practical for service robot systems. Taken together, this work will make a significant step towards creating general-purpose service robots.

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AAAI Conference 2021 Conference Paper

Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks

Yuqian Jiang
Suda Bharadwaj
Bo Wu
Rishi Shah
Ufuk Topcu
Peter Stone

In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for averagereward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines.

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IROS Conference 2020 Conference Paper

Deep R-Learning for Continual Area Sweeping

Rishi Shah
Yuqian Jiang
Justin W. Hart
Peter Stone 0001

Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics. We also present a video demonstration on a real service robot.

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JAIR Journal 2020 Journal Article

Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog

Jesse Thomason
Aishwarya Padmakumar
Jivko Sinapov
Nick Walker
Yuqian Jiang
Harel Yedidsion
Justin Hart
Peter Stone

In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clari_cation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the y while completing a real-world task.

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ICRA Conference 2019 Conference Paper

Improving Grounded Natural Language Understanding through Human-Robot Dialog

Jesse Thomason
Aishwarya Padmakumar
Jivko Sinapov
Nick Walker 0001
Yuqian Jiang
Harel Yedidsion
Justin W. Hart
Peter Stone 0001

Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically-continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.

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ICAPS Conference 2019 Conference Paper

Open-World Reasoning for Service Robots

Yuqian Jiang
Nick Walker 0001
Justin W. Hart
Peter Stone 0001

A service robot accepting verbal commands from a human operator is likely to encounter requests that reference objects not currently represented in its knowledge base. In domestic or office settings, the construction of a complete knowledge base would be cumbersome and unlikely to succeed in most real-world deployments. The world that such a robot operates in is thus “open” in the sense that some objects that it must act on in the real world are not described in its internal representation. However, when an operator gives a command referencing an object that the robot has not yet observed (and thus not incorporated into its knowledge base), we can think of the object as being hypothetical to the robot. This paper presents a novel method for closing the robot’s world model for planning purposes by introducing hypothetical objects into the robot’s knowledge base, reasoning about these hypothetical objects, and acting on these hypotheses in the real world. We use our implementation of this method on a domestic service robot as an illustrative demonstration to explore how it works in practice.

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IROS Conference 2019 Conference Paper

Task-Motion Planning with Reinforcement Learning for Adaptable Mobile Service Robots

Yuqian Jiang
Fangkai Yang
Shiqi Zhang 0001
Peter Stone 0001

Task-motion planning (TMP) addresses the problem of efficiently generating executable and low-cost task plans in a discrete space such that the (initially unknown) action costs are determined by motion plans in a corresponding continuous space. A task-motion plan for a mobile service robot that behaves in a highly dynamic domain can be sensitive to domain uncertainty and changes, leading to suboptimal behaviors or execution failures. In this paper, we propose a novel framework, TMP-RL, which is an integration of TMP and reinforcement learning (RL), to solve the problem of robust TMP in dynamic and uncertain domains. The robot first generates a low-cost, feasible task-motion plan by iteratively planning in the discrete space and updating relevant action costs evaluated by the motion planner in continuous space. During execution, the robot learns via model-free RL to further improve its task-motion plans. RL enables adaptability to the current domain, but can be costly with regards to experience; using TMP, which does not rely on experience, can jump-start the learning process before executing in the real world. TMP-RL is evaluated in a mobile service robot domain where the robot navigates in an office area, showing significantly improved adaptability to unseen domain dynamics over TMP and task planning (TP)-RL methods.

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AAMAS Conference 2017 Conference Paper

Multirobot Symbolic Planning under Temporal Uncertainty

Shiqi Zhang
Yuqian Jiang
Guni Sharon
Peter Stone

Multirobot symbolic planning (MSP) aims at computing plans, each in the form of a sequence of actions, for a team of robots to achieve their individual goals while minimizing overall cost. Solving MSP problems requires modeling limited domain resources (e. g. , corridors that allow at most one robot at a time) and the possibility of action synergy (e. g. , multiple robots going through a door after a single door-opening action). However, the temporal uncertainty that propagates over actions, such as delays caused by obstacles in navigation actions, makes it challenging to plan for resource sharing and realizing synergy in a team of robots. This paper, for the first time, introduces the problem of MSP under temporal uncertainty (MSPTU). We present a novel, iterative inter-dependent planning (IIDP) algorithm, including two configurations (simple and enhanced), for solving general MSPTU problems. We then focus on multirobot navigation tasks, presenting a full instantiation of IIDP that includes a new algorithm for computing conditional plan cost under temporal uncertainty and a novel shifted-Poisson distribution for accumulating temporal uncertainty over actions. The algorithms have been implemented both in simulation and on real robots. We observed a significant reduction in overall cost compared to baselines in which robots do not communicate or model temporal uncertainty. CCS Concepts •Computing methodologies → Robotic planning; Multi-agent planning; Planning under uncertainty;

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