1 citations · 2 across the 21 of their papers we have counts for
23 papers
La-JEPA: Compact Robot Policies with Latent World Modeling
Mateo Clémente, George Z. Xue, Amin Abyaneh +5
World-action models adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie policy architecture and inference c…
StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation
Jinbang Huang, Yuanzhao Hu, Zhiyuan Li +6
Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminate the current skill and adva…
Faster-WAM: Do World Action Models Need Deep Action Modules?
Liheng Ma, Rui Heng Yang, Amin Abyaneh +8
World Action Models (WAMs) build on pretrained video models, whose representations are grounded in physical dynamics and provide a natural basis for action prediction. Despite this…
RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning
Jinbang Huang, Yuanzhao Hu, Zhiyuan Li +6
Long-horizon robotic tasks require a breadth of capabilities beyond what any single existing robot control policy can reliably provide. Combining heterogeneous policies with comple…
Anticipate Before Acting: Future-State-Conditioned Vision-Language Navigation
Lingfeng Zhang, Zhanguang Zhang, Liheng Ma +2
End-to-end vision-language navigation (VLN) with causal vision-language models maps instructions and egocentric observations directly to actions, but standard behavior cloning supe…
Uncertainty-Aware Gaussian Map for Vision-Language Navigation
Jianzhe Gao, Rui Liu, Yuxuan Xu +6
Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agents commonly encounter percep…
Robo-Cortex: A Self-Evolving Embodied Agent via Dual-Grain Cognitive Memory and Autonomous Knowledge Induction
Nga Teng Chan, Yi Zhang, Yechi Liu +9
The ability to navigate and interact with complex environments is central to real-world embodied agents, yet navigation in unseen environments remains challenging due to "experient…
H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model
Jinbang Huang, Wenyuan Chen, Zhiyuan Li +9
World models are becoming central to robotic planning and control by predicting future state transitions. Existing approaches mainly rely on visual, latent, or language prediction,…
Do World Action Models Generalize Better than VLAs? A Robustness Study
Zhanguang Zhang, Zhiyuan Li, Behnam Rahmati +11
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response…
Distracted Robot: How Visual Clutter Undermine Robotic Manipulation
Amir Rasouli, Montgomery Alban, Sajjad Pakdamansavoji +4
In this work, we propose an evaluation protocol for examining the performance of robotic manipulation policies in cluttered scenes. Contrary to prior works, we approach evaluation…
It Takes Two: Your GRPO Is Secretly DPO
Yihong Wu, Liheng Ma, Lei Ding +9
GRPO has emerged as a prominent reinforcement learning algorithm for post-training LLMs. Unlike critic-based methods, GRPO computes advantages by estimating the \emph{value baselin…
Embodied Arena: A Comprehensive, Unified, and Evolving Evaluation Platform for Embodied AI
Fei Ni, Min Zhang, Pengyi Li +34
Embodied AI development significantly lags behind large foundation models due to three critical challenges: (1) lack of systematic understanding of core capabilities needed for Emb…
Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation
Jinbang Huang, Zhiyuan Li, Yuanzhao Hu +4
Large Language Models (LLMs) have recently shown strong promise for robotic task planning, particularly through automatic planning domain generation. However, prior approaches larg…
OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Yuecheng Liu, Dafeng Chi, Shiguang Wu +10
Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction,…
Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
Mohammad Ali Alomrani, Yingxue Zhang, Derek Li +14
Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoni…
One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
Jinbang Huang, Yixin Xiao, Zhanguang Zhang +3
Pre-trained large language models (LLMs) show promise for robotic task planning but often struggle to guarantee correctness in long-horizon problems. Task and motion planning (TAMP…
Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation
Lingfeng Zhang, Yuecheng Liu, Zhanguang Zhang +19
Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, enabling agents to leverage commonsense…
SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning
Yuecheng Liu, Dafeng Chi, Shiguang Wu +13
Spatial reasoning is an essential problem in embodied AI research. Efforts to enhance spatial reasoning abilities through supplementary spatial data and fine-tuning have proven lim…
E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Prediction
Saurabh Bodhe, Zhanguang Zhang, Atia Hamidizadeh +3
Pre-routing slack prediction remains a critical area of research in Electronic Design Automation (EDA). Despite numerous machine learning-based approaches targeting this task, ther…
The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation
Reza Moravej, Saurabh Bodhe, Zhanguang Zhang +6
Logic synthesis is a crucial phase in the circuit design process, responsible for transforming hardware description language (HDL) designs into optimized netlists. However, traditi…
ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models
Lingfeng Zhang, Yuening Wang, Hongjian Gu +12
Recent advancements in Large Language Models (LLMs) have spurred numerous attempts to apply these technologies to embodied tasks, particularly focusing on high-level task planning…
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
Joseph Cotnareanu, Zhanguang Zhang, Hui-Ling Zhen +2
Efficiently determining the satisfiability of a boolean equation -- known as the SAT problem for brevity -- is crucial in various industrial problems. Recently, the advent of deep…
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu +7
Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. Th…