11 papers
RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?
Zhenxuan Fan, Bo Zhang, Yutong Lin +9
Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchmarks mainly evaluate task compl…
EmbodiedSkills: World Action Model-augmented Cascaded Skills for Vision-Language-Action Agent
Wei Wang, Wenqiao Zhang, Yutong Lin +14
Vision-language-action (VLA) policies generate executable robot actions, but plausible proposals can have different consequences for the task. We present EmbodiedSkills, a skill-ba…
UniPhysGen: Unified Physical Grounding for Simulation-Ready 3D Assets
Xian Li, Rong Wei, Lujie Yang +6
Physically grounded 3D assets are increasingly important for embodied AI and robotic simulation. However, most existing 3D assets lack unified physical semantics, including articul…
SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
Zhengxi Lu, Zhiyuan Yao, Jinyang Wu +7
Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting L…
ReMemNav: Memory-Based Decision Correction and Target Verification for Zero-Shot Object Navigation
Feng Wu, Wei Zuo, Wenliang Yang +3
Zero-shot object navigation requires agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training. Despite the commonsense reasoning abil…
Ego-Grounding for Personalized Question-Answering in Egocentric Videos
Junbin Xiao, Shenglang Zhang, Pengxiang Zhu +1
We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding - the ability to understand the came…
CORE: Code-based Inverse Self-Training Framework with Graph Expansion for Virtual Agents
Keyu Wang, Bingchen Miao, Wendong Bu +7
The development of Multimodal Virtual Agents has made significant progress through the integration of Multimodal Large Language Models. However, mainstream training paradigms face…
PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models
Haoyu Zheng, Yun Zhu, Yuqian Yuan +4
Strategic planning is critical for multi-step reasoning, yet compact Large Language Models (LLMs) often lack the capacity to formulate global strategies, leading to error propagati…
OmniMoGen: Unifying Human Motion Generation via Learning from Interleaved Text-Motion Instructions
Wendong Bu, Kaihang Pan, Yuze Lin +6
Large language models (LLMs) have unified diverse linguistic tasks within a single framework, yet such unification remains unexplored in human motion generation. Existing methods a…
Arcadia: Toward a Full-Lifecycle Framework for Embodied Lifelong Learning
Minghe Gao, Juncheng Li, Yuze Lin +15
We contend that embodied learning is fundamentally a lifecycle problem rather than a single-stage optimization. Systems that optimize only one link (data collection, simulation, le…
Towards Physically Executable 3D Gaussian for Embodied Navigation
Bingchen Miao, Rong Wei, Zhiqi Ge +8
3D Gaussian Splatting (3DGS), a 3D representation method with photorealistic real-time rendering capabilities, is regarded as an effective tool for narrowing the sim-to-real gap. H…