Author name cluster

Jun Shao

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4 papers

2 author rows

AAAI Conference 2025 Conference Paper

MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents

Congchi Yin
Feng Li
Shu Zhang
Zike Wang
Jun Shao
Piji Li
Jianhua Chen
Xun Jiang

The clinical diagnosis of most mental disorders primarily relies on the conversations between psychiatrist and patient. The creation of such diagnostic conversation datasets is promising to boost the AI mental healthcare community. However, directly collecting the conversations in real diagnosis scenarios is near impossible due to stringent privacy and ethical considerations. To address this issue, we seek to synthesize diagnostic conversation by exploiting anonymized patient cases that are easier to access. Specifically, we design a neuro-symbolic multi-agent framework for synthesizing the diagnostic conversation of mental disorders with large language models. It takes patient case as input and is capable of generating multiple diverse conversations with one single patient case. The framework basically involves the interaction between a doctor agent and a patient agent, and generates conversations under symbolic control via a dynamic diagnosis tree. By applying the proposed framework, we develop the largest Chinese mental disorders diagnosis dataset MDD-5k. This dataset is built upon 1000 real, anonymized patient cases by cooperating with Shanghai Mental Health Center and comprises 5000 high-quality long conversations with diagnosis results and treatment opinions as labels. To the best of our knowledge, it's also the first labeled dataset for Chinese mental disorders diagnosis. Human evaluation demonstrates the proposed MDD-5k dataset successfully simulates human-like diagnostic process of mental disorders.

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ICRA Conference 2024 Conference Paper

FLTRNN: Faithful Long-Horizon Task Planning for Robotics with Large Language Models

Jiatao Zhang
Lanling Tang
Yufan Song
Qiwei Meng
Haofu Qian
Jun Shao
Wei Song 0008
Shiqiang Zhu

Recent planning methods based on Large Language Models typically employ the In-Context Learning paradigm. Complex long-horizon planning tasks require more context(including instructions and demonstrations) to guarantee that the generated plan can be executed correctly. However, in such conditions, LLMs may overlook(unfaithful) the rules in the given context, resulting in the generated plans being invalid or even leading to dangerous actions. In this paper, we investigate the faithfulness of LLMs for complex long-horizon tasks. Inspired by human intelligence, we introduce a novel framework named FLTRNN. FLTRNN employs a language-based RNN structure to integrate task decomposition and memory management into LLM planning inference, which could effectively improve the faithfulness of LLMs and make the planner more reliable. We conducted experiments in VirtualHome household tasks. Results show that our model significantly improves faithfulness and success rates for complex long-horizon tasks. Website at https://tannl.github.io/FLTRNN.github.io/

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ICRA Conference 2024 Conference Paper

Leveraging the efficiency of multi-task robot manipulation via task-evoked planner and reinforcement learning

Haofu Qian
Haoyang Zhang
Jun Shao
Jiatao Zhang
Jason Jianjun Gu
Wei Song 0008
Shiqiang Zhu

Multi-task learning has expanded the boundaries of robotic manipulation, enabling the execution of increasingly complex tasks. However, policies learned through reinforcement learning exhibit limited generalization and narrow distributions, which restrict their effectiveness in multi-task training. Addressing the challenge of obtaining policies with generalization and stability represents a non-trivial problem. To tackle this issue, we propose a planning-guided reinforcement learning method. It leverages a task-evoked planner(TEP) and a reinforcement learning approach with planner’s guidance. TEP utilizes reusable samples as the source, with the aim of learning reachability information across different task scenarios. Then in reinforcement learning, TEP assesses and guides the Actor towards better outputs and smoothly enhances the performance in multi-task benchmarks. We evaluate this approach within the Meta-World framework and compare it with prior works in terms of learning efficiency and effectiveness. Depending on experimental results, our method has more efficiency, higher success rates, and demonstrates more realistic behavior.

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IROS Conference 2023 Conference Paper

Towards Safe and Aggressive Motion Generation for Dynamic Targets Pick-and-Place

Jun Shao
Jianfeng Liao
Han Li
Haoyang Zhang
Shiqiang Zhu
Wei Song 0008
Yinchun Huang

In this paper, we present a framework to generate time-optimal trajectories for dynamic target pick-and-place tasks. We develop an optimization-based trajectory generation method for manipulators, which can conduct spatial-temporal deformation under user-defined requirements. We formulate the problem of dynamic target pick-and-place, in which the trajectory duration and jerk are optimized and terminal states are adjusted instead of being fixed. The motions are constrained within the mechanical limits and to avoid collisions. Constraints transcription is adopted to convert constraints to weighted penalties. Then the problem can be solved based on the trajectory generation method with a high-level optimizer. We integrate the proposed method with online perception into a robot arm platform, in which a conveyor belt is used to transport the objects. Simulations and real-world experiments are conducted under a range of object speeds. Results show that the proposed method achieves online grasping under the object velocity up to 0. 5m/s with an average computing time of 190ms.

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