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cs.RO2026
EmbodiedLab: Benchmarking Embodied Agents That Act to Know
Hongcheng Gao, Zelin Zheng, Alex Zhou +18
Embodied agents are expected to accomplish real-world goals by deciding for themselves how and when to interact with their surroundings. For many such goals, changing the physical…
cs.RO2026
Make Code as Policy Great Again: Frontier Agents Write, Call, and Evolve Robot Tools
Shijia Ge, Alex Zhou, Jianshu Zeng +12
Frontier models can control robots, but reasoning through every reach, grasp, and retreat makes manipulation slow and token-intensive. We revisit code as policy with a different di…
cs.RO2026
Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence
Hongcheng Gao, Jingjing Zhou, Zelin Zheng +10
Vision-language-action (VLA) and world-action (WAM) models map observations and instructions directly to robot actions. This directness ties a policy to training: minor layout or v…