9 papers
Cop number of -free graphs without a claw or a fork
Uttam K. Gupta, Dinabandhu Pradhan, Rashmi Rekha Swain
In 2019, Liu proved that the cop number of any -free graph is at most . In 2026, Clow and Meger improved and generalized the result of Liu by showing that every c…
LAMP: Latent Motion Prior-Guided Real-World Learning for Dexterous Hand Manipulation
Xinye Yang, Zhiyuan Ma, Hongze Yu +5
Real-world learning for dexterous hands remains brittle because high-dimensional hand actions amplify imitation errors and make reinforcement-learning exploration prone to contact-…
STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning
Zhihao Liu, Qiuyi Gu, Yitao Wang +16
Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Ef…
USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI
Hongzhi Zang, Shu'ang Yu, Hao Lin +14
Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitraril…
ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation
Qiuyi Gu, Yuze Sheng, Jincheng Yu +7
3D scene graphs have empowered robots with semantic understanding for navigation and planning. However, current functional scene graphs primarily focus on static element detection,…
MARSHAL: Incentivizing Multi-Agent Reasoning via Self-Play with Strategic LLMs
Huining Yuan, Zelai Xu, Zheyue Tan +10
Developing Large Language Models (LLMs) to cooperate and compete effectively within multi-agent systems (MASs) is a critical step towards more advanced intelligence. While reinforc…
JuggleRL: Mastering Ball Juggling with a Quadrotor via Deep Reinforcement Learning
Shilong Ji, Yinuo Chen, Chuqi Wang +9
Aerial robots interacting with objects must perform precise, contact-rich maneuvers under uncertainty. In this paper, we study the problem of aerial ball juggling using a quadrotor…
VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
Zelai Xu, Zhexuan Xu, Xiangmin Yi +7
Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain limited to single-agent or text-onl…
What Can RL Bring to VLA Generalization? An Empirical Study
Jijia Liu, Feng Gao, Bingwen Wei +5
Large Vision-Language Action (VLA) models have shown significant potential for embodied AI. However, their predominant training via supervised fine-tuning (SFT) limits generalizati…