13 papers
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
Chenyuan Zhou, Yining Hong, Huang Huang +9
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitl…
FACT: A Multimodal Force-Action-Track Foundation Model for Human-to-Robot Dexterity
Zhi Cao, Yining Hong, Jiaolong Yang +4
Human demonstrations offer a scalable alternative to costly robot data collection for dexterous manipulation, but most human datasets lack force measurements. Dexterous manipulatio…
AutoAgent0: An Agentic Runtime for Safe Closed-Loop Driving
Jiageng Liu, Seth Z. Zhao, Zewei Zhou +9
Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments. Although end-to-end driving polic…
UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement
Fang Wu, Da Xing, Yanjie Huang +16
Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified…
TMem: Learning Test-Time Memory for Robotics
Yize Liu, Huang Huang, Yining Hong +4
Memory-dependent robotic manipulation requires policies to use information that is no longer available in the current observation. Retaining history alone is insufficient: memory m…
MindTopo: Can Foundation Models Reason in Topological Space?
Yunfei Ge, Anbang Liu, Qineng Wang +9
Spatial reasoning depends not only on metric properties such as distance, angle, and shape, but also on topological relations that remain invariant under continuous deformation. Co…
Trimming the Long-Tail of Visual World Modeling Evaluation
Bingxuan Li, Yining Hong, Cheng Qian +6
Physical interactions follow a long-tailed distribution: a set of common and regular interactions dominates human experience and visual data, while a broad spectrum of rare and irr…
ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop
Yining Hong, Jiageng Liu, Han Yin +5
Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of action. Rather than passi…
Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs
Yining Hong, Huang Huang, Manling Li +4
Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistake…
Envision: Embodied Visual Planning via Goal-Imagery Video Diffusion
Yuming Gu, Yizhi Wang, Yining Hong +9
Embodied visual planning aims to enable manipulation tasks by imagining how a scene evolves toward a desired goal and using the imagined trajectories to guide actions. Video diffus…
Embodied Web Agents: Bridging Physical-Digital Realms for Integrated Agent Intelligence
Yining Hong, Rui Sun, Bingxuan Li +7
AI agents today are mostly siloed - they either retrieve and reason over vast amount of digital information and knowledge obtained online; or interact with the physical world throu…
3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model
Wenbo Hu, Yining Hong, Yanjun Wang +7
Humans excel at performing complex tasks by leveraging long-term memory across temporal and spatial experiences. In contrast, current Large Language Models (LLMs) struggle to effec…
SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation
Yining Hong, Beide Liu, Maxine Wu +9
Human beings are endowed with a complementary learning system, which bridges the slow learning of general world dynamics with fast storage of episodic memory from a new experience.…