RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents
A foundation model should not act in isolation as an embodied agent. Yet, existing methods often optimize individual components of the agent stack, such as memory, context, skills, or action interfaces, rather than treating the supporting system itself as a unified policy. Moreover, interaction alone does not yield self-improvement unless execution experience is converted into persistent, validated system changes. We therefore propose RoboFoundry, the first embodied agentic framework that formulates this process as Self-Evolving System-as-Policy. RoboFoundry diagnoses capability gaps in decision-making and memory management, converts execution traces into validated task-specific system up...
| Authors | Jingsong Liang, Shuhao Liao, Shizhe Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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