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Bingcheng Dong

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ICRA Conference 2025 Conference Paper

Knowledge-Driven Visual Target Navigation: Dual Graph Navigation

Shiyao Li
Ziyang Meng
Jiansong Pei
Jiahao Chen
Bingcheng Dong
Guangsheng Li
Shenglan Liu 0001
Feilong Wang

In unknown environments, navigating a robot by a given image to a specific location or instance is critical and challenging. The existing end-to-end approaches require simultaneous implicit learning of multiple subtasks, and modular approaches depend on metric information. Both approaches face high computational demands, often leading to difficulties in real-time updates and limited generalization, making them challenging to implement on resource-constrained devices. To address these challenges, we propose Dual Graph Navigation (DGN), a knowledge-driven, lightweight image instance navigation framework. DGN builds an External Knowledge Graph (EKG) from small-scale datasets to capture prior object correlations, efficiently guiding target exploration. During exploration, DGN builds an Internal Knowledge Graph (IKG) using an instance-aware module, which records explored objects based on reachability relationships rather than precise metric information. The IKG dynamically updates the EKG, enhancing the robot's adaptability to the current environment. Together, they realize topological perception and reduce computational overhead. Furthermore, unlike approaches characterized by over-dependence between components, DGN employs a plug-and-play modular design that allows independent training and flexible replacement of functional modules, effectively enhancing generalization performance while reducing training and deployment costs. Experiments illustrate that DGN generalizes well in different simulation environments (AI2-THOR, Habitat), achieving state-of-the-art performance on the ProcTHOR-10K dataset. It is compatible with three distinct real-world robot platforms, including edge computing devices without CUDA support. It exhibits a decision-making speed of 3. 8 to 5. 5 times over baseline methods. Further details can be found on the project page: https://dogplanningloyo.github.io/DGN/.

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NeurIPS Conference 2025 Conference Paper

rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset

Yifei Liu
Li Lyna Zhang
Yi Zhu
Bingcheng Dong
Xudong Zhou
Ning Shang
Fan Yang
Cheng Li

Advancing code reasoning in large language models (LLMs) is fundamentally limited by the scarcity of high-difficulty datasets, especially those with verifiable input-output test cases necessary for rigorous solution validation at scale. We introduce rStar-Coder, which significantly improves LLM code reasoning capabilities by constructing a large-scale, verified dataset of 418K competition-level code problems, 580K long-reasoning solutions along with rich test cases of varying difficulty. This is achieved through three core contributions: (1) we curate competitive programming code problems and solutions to synthesize new, solvable problems; (2) we introduce a reliable input-output test case synthesis pipeline that decouples the generation into a three-step input generation method and a mutual verification mechanism for effective output labeling; (3) we augment problems with high-quality, test-case-verified long-reasoning solutions. Extensive experiments on Qwen models (1. 5B-14B) across various code reasoning benchmarks demonstrate the superiority of rStar-Coder dataset, achieving leading performance comparable to frontier reasoning LLMs with significantly smaller model sizes. On LiveCodeBench, rStar-Coder improves Qwen2. 5-7B from 17. 4% to an impressive 57. 3%, and Qwen2. 5-14B from 23. 3% to 62. 5%, surpassing o3-mini (low) by 3. 1%. On the more challenging USA Computing Olympiad, our 7B model achieves an average pass@1 accuracy of 16. 15%, outperforming the frontier-level QWQ-32B. rStar-Coder dataset is publicly available at https: //huggingface. co/datasets/microsoft/rStar-Coder.

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