7 citations · 17 across the 20 of their papers we have counts for
21 papers
Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data
Ye Wang, Pei Lin, Xiong-Hui Chen +12
Learning generalizable robot manipulation policies requires large-scale and diverse demonstration data. Egocentric human manipulation videos offer rich scene and task diversity, an…
IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning
Yiyan Li, Guanli Liu, Renata Borovica-Gajic +6
Database tuning is critical for achieving high performance in modern database management systems (DBMSs). Existing methods typically optimize a single component---knobs, indexes, o…
Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision
Haoyang Li, Guanlin Li, Youhe Feng +9
Cross-embodiment transfer in vision-language-action (VLA) models remains challenging because low-level state and action spaces differ fundamentally across robot platforms. We obser…
Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
Haoqi Yuan, Zhixuan Liang, Anzhe Chen +20
Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale. In this report, we i…
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Jie Zhang, Xiaoyue Chen, Anzhe Chen +36
We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically ground…
UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
Yufei Jia, Zhanxiang Cao, Mingrui Yu +48
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-ce…
Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
Qiuyue Wang, Mingsheng Li, Jian Guan +37
Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generali…
Towards Reliable Agentic Progressive Text-to-Visualization with Verification Rules
Wenxin Xu, Chen Jason Zhang, Xiaoyong Wei +4
Text-to-Visualization (Text-to-Vis) translates natural language queries into visualization query languages, enabling non-expert users to perform data analysis. However, most existi…
ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation
Youhe Feng, Hansen Shi, Haoyang Li +7
Long-horizon robotic manipulation requires dense feedback that reflects how a task advances through its procedural stages, not merely whether the final outcome is successful. Exist…
From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models
Yihan Lin, Haoyang Li, Yang Li +4
Latent actions serve as an intermediate representation that enables consistent modeling of vision-language-action (VLA) models across heterogeneous datasets. However, approaches to…
TokenPB: Enabling VLA Action Self-Verification via VLM Token Probability Bucketing
Chen Zhao, Zhuoran Wang, Haoyang Li +6
Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candidates. Prior work often relies o…
A Query Optimization Method Utilizing Large Language Models
Zhiming Yao, Haoyang Li, Jing Zhang +2
Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional…
VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering
Yanling Wang, Yihan Zhao, Xiaodong Chen +7
Large vision-language models (LVLMs) have demonstrated remarkable achievements, yet the generation of non-factual responses remains prevalent in fact-seeking question answering (QA…
LLMIA: An Out-of-the-Box Index Advisor via In-Context Learning with LLMs
Xinxin Zhao, Xinmei Huang, Haoyang Li +7
Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimat…
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Haoyang Li, Shang Wu, Xiaokang Zhang +9
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements…
Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL
Hanbing Liu, Haoyang Li, Xiaokang Zhang +5
Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it…
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Yiyan Li, Haoyang Li, Zhao Pu +6
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance. However, traditional tuning methods often follow a Try-Collect-Adjust a…
FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models
Yanling Wang, Haoyang Li, Hao Zou +4
Despite advancements in large language models (LLMs), non-factual responses still persist in fact-seeking question answering. Unlike extensive studies on post-hoc detection of thes…
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
Xinmei Huang, Haoyang Li, Jing Zhang +7
Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achie…
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu +7
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA)…
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Haoyang Li, Jing Zhang, Cuiping Li +1
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of p…