Ant Lingbo LingBot-VA Paper Accepted by Top Robotics Conference RSS 2026, Enabling Robots to Reason While Acting
Ant Group's LingBot-VA, a causal world modeling framework for robot control, has been accepted into the prestigious Robotics: Science and Systems (RSS) 2026 conference. This framework enables robots to predict environmental changes before acting, mimicking human-like observation, judgment, and action. LingBot-VA utilizes a Mixture-of-Transformers architecture and has demonstrated high success rates on simulated and real-world robotic tasks, showcasing strong data efficiency and generalization capabilities. The research aims to advance robots from simple instruction followers to systems with enhanced environmental understanding and autonomous decision-making. AI
IMPACT Advances robot control by enabling predictive world modeling, potentially leading to more autonomous and adaptable robotic systems.