Author name cluster

Xiaodong Yi 0002

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.

4 papers

1 author row

IROS Conference 2024 Conference Paper

DiaGBT: An Explainable and Evolvable Robot Control Framework using Dialogue Generative Behavior Trees

Jinde Liang
Yuan Chang
Qian Wang
Yanzhen Wang
Xiaodong Yi 0002

Manipulating robots using natural language is the preferred way for non-technical specialists. The challenge lies in reliability and adaptability especially when robots operate in unstructured surroundings. In this paper, we propose a novel framework called Dialogue Generative Behavior Trees (DiaGBT). Natural language instructions from human operators are transformed into behavior trees (BTs) and further executed by robots. Compared to the emerging Large Language Models (LLMs), DiaGBT is comparable in terms of semantic understanding but more lightweight, since the parsing rules are produced by LLM but tailored for task-correlated instructions. Besides, DiaGBT allows multi-round human-robot interaction, where robots learn reusable skills in real time. For evaluation, we generate a dataset with 4k instruction-BT pairs covering 4 different scenarios. On average, DiaGBT reaches over 90% parsability and 80% plausibility. Similar results on the VEIL-500 dataset outperform the current state of the art.

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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.

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IROS Conference 2021 Conference Paper

micROS. BT: An Event-Driven Behavior Tree Framework for Swarm Robots

Yunlong Wu 0002
Jinghua Li
Huadong Dai
Xiaodong Yi 0002
Yanzhen Wang
Xuejun Yang

In this paper, we propose micROS. BT, an event-driven behavior tree (BT) framework aiming at supporting swarm-robot coordination. Compared with other BT frame-works, micROS. BT implements the event-driven way under the multi-thread mode, which can effectively save computing resources. Moreover, in order to ensure swarm-robot coordination, we optimize the implementation of the traditional blackboard and propose the multi-mode blackboard, which supports inner-tree, inter-tree, and inter-robot data sharing. Furthermore, considering the limited modularity of a single tree, micROS. BT realizes a mechanism called hierarchical tree management which involves inter-tree notifying and waiting functionalities, while ensuring that each tree is independent and self-scheduled. The effectiveness of micROS. BT is verified by simulation and real-robot experiments for different system settings, showing that a substantial improvement is achieved in comparison with the traditional BT implementations.

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IROS Conference 2020 Conference Paper

An Actor-based Programming Framework for Swarm Robotic Systems

Wei Yi
Bin Di
Ruihao Li 0001
Huadong Dai
Xiaodong Yi 0002
Yanzhen Wang
Xuejun Yang

Programming cooperative tasks for autonomous swarm robotic systems has always been challenging. In this paper, we introduce a concept ‘Actor’, as a virtualization for robot platforms. Every robot platform in the swarm robotic system carries out the task and interacts with others as an Actor. We designed an Actor-based framework for the management of autonomous swarm robotic systems including modules and interfaces for the Actor, the collective Actor, and task management. The Actor-based framework enables task developers to explicitly model cooperative tasks without intricacies about the detailed robotic algorithms or the specific robot brands, and eases the burden on robotic algorithm developers by providing common functionalities. The proposed framework is implemented in C++ and validated quantitatively and qualitatively with a swarm of thirty drones by simulations and a swarm of ten drones by in-field tests.

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