7 citations · 9 across the 9 of their papers we have counts for
12 papers
NavHarness: Adaptive Goals for Agentic Vision-Language Navigation
Haoxiang Shi, Zaijing Li, Muhe Ding +3
Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents offer a promising basis for…
Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model
Zaijing Li, Rui Shao, Bing Hu +3
Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, yet adapting them to new tasks and domains remains inefficient: existing met…
From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model
Bing Hu, Zaijing Li, Rui Shao +4
Vision-Language-Action (VLA) models often suffer from performance degradation under distribution shifts, as they struggle to learn generalized behavior representations across varyi…
Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation
Zaijing Li, Bing Hu, Rui Shao +5
Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a Vision-Language backbone for perception…
HiconAgent: History Context-aware Policy Optimization for GUI Agents
Xurui Zhou, Gongwei Chen, Yuquan Xie +6
Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can impr…
DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Haoxiang Shi, Xiang Deng, Zaijing Li +3
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces. Existing VLN-CE approaches typic…
Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skills
Yuquan Xie, Zaijing Li, Rui Shao +5
Recent efforts to leverage the Multi-modal Large Language Model (MLLM) as GUI agents have yielded promising outcomes. However, these agents still struggle with long-horizon tasks i…
Optimus-3: Dual-Router Aligned Mixture-of-Experts Agent with Dual-Granularity Reasoning-Aware Policy Optimization
Zaijing Li, Yuquan Xie, Rui Shao +5
Developing generalist agents capable of solving open-ended tasks in visually rich, dynamic environments remains a core pursuit of embodied AI. While Minecraft has emerged as a comp…
Spatial Understanding from Videos: Structured Prompts Meet Simulation Data
Haoyu Zhang, Meng Liu, Zaijing Li +4
Visual-spatial understanding, the ability to infer object relationships and layouts from visual input, is fundamental to downstream tasks such as robotic navigation and embodied in…
Optimus-2: Multimodal Minecraft Agent with Goal-Observation-Action Conditioned Policy
Zaijing Li, Yuquan Xie, Rui Shao +3
Building an agent that can mimic human behavior patterns to accomplish various open-world tasks is a long-term goal. To enable agents to effectively learn behavioral patterns acros…
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
Zaijing Li, Yuquan Xie, Rui Shao +3
Building a general-purpose agent is a long-standing vision in the field of artificial intelligence. Existing agents have made remarkable progress in many domains, yet they still st…
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought
Zaijing Li, Gongwei Chen, Rui Shao +3
Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potentia…