ACSE: Agentic Collaborative Self-Evolution for Embodied Intelligence

Robot policies are typically trained on fixed task and data distributions, with improvements primarily driven by data scaling. Although data scaling has improved generalization to complex tasks, existing approaches largely rely on passive learning from predefined tasks, limiting proactive challenge discovery and capability expansion. We propose Agentic Collaborative Self-Evolution (ACSE), a framework that couples task generation with agent learning in a closed loop, enabling embodied agents to discover and overcome their limitations. ACSE comprises a Scene Generator and a Robotic Solver. The Robotic Solver integrates high-level task reasoning, world modeling, and action execution, while the Scene Generator explores the Solver’s current capability boundary to generate challenging, informative scenarios. The Solver improves through reinforcement learning using closed-loop rollouts, and newly exposed capability gaps guide the Generator’s evolution, allowing scenario difficulty and agent capabilities to co-evolve. Unlike data scaling under fixed task distributions or curricula with predefined difficulty schedules, ACSE uses interaction feedback to adapt the training distribution to the agent’s evolving capabilities. Experiments show that ACSE continually generates novel, informative challenges and substantially improves generalization across complex long-horizon manipulation tasks and out-of-distribution settings. These results suggest a promising path toward autonomous challenge discovery and continual self-improvement in embodied intelligence.