How reliably can today’s robot policies complete tasks when the world changes mid-execution? In real-world robotics, things rarely go exactly as planned. Objects move, unexpected obstacles appear, and observations change as robots interact with their surroundings. Understanding how well robot policies handle these changes is essential for deploying Physical AI in the real world. In our new paper, LIBERO-MAX: Do Robot Policies Adapt When the World Changes?, we introduce a benchmark designed to study this question. We evaluate 14 state-of-the-art robot policies across 8,000 cases, introducing controlled changes after the robot has already begun acting. The results suggest that adapting to a changing world remains a key challenge for today’s robot policies. Task success drops by 11–26 percentage points, particularly when geometry or observations change, and simply updating policies more frequently does not close the gap. LIBERO-MAX provides a systematic way to measure these challenges, helping identify where current policies fall short and guiding the development of more adaptive and robust Physical AI systems. 📄 Paper: 🌐 Benchmark and project website: 💻 Code: Work led by @YunbeiZhang, in collaboration with Zijian Jin, Yuanzhe Liu, Janet Wang, Xilun Zhang, Yuyou Zhang, Zhenyu Zhang, Daoan Zhang, Shuaicheng Niu, Gen Li, Jianfei Yang, Jihun Hamm, Ismini Lourentzou, Weirui Ye, Bo Liu, and Peter Stone. #Robotics# #PhysicalAI# #RobotLearning# #RobotFoundationModels# #AutonomousSystems#