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This paper introduces SHAPER, a self-evolving framework for embodied agents that enhances performance without modifying model parameters by evolving reusable skills and a context-code harness through target-environment rollouts. The approach addresses the limitations of traditional fine-tuning and reinforcement learning methods, which require extensive data and training, by allowing agents to adapt in fixed-interface settings. Evaluation on VLABench and ESI-Bench demonstrates that SHAPER outperforms existing methods, suggesting a viable pathway for developing adaptable agents in resource-constrained environments.
SHAPER enables embodied agents to self-evolve their skills and context without retraining, unlocking new possibilities for adaptation in fixed-interface scenarios.
Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces, and execution harness surrounding the model. While supervised fine-tuning and reinforcement learning can adapt agents to new environments, they require additional data, rewards, and training runs; meanwhile, many train-free code-centric approaches rely on programmable robot APIs that may be unavailable in fixed-interface settings. We propose SHAPER, a self-evolving framework for train-free embodied adaptation that keeps model parameters frozen and improves the non-parametric agent system by evolving reusable skills and a context-code harness through target-environment rollouts. In SHAPER, the same frozen model can serve as both planner and optimizer, refining its external skills and context-code harness without parameter updates. We evaluate SHAPER on VLABench and ESI-Bench, covering embodied agents with different low-level action interfaces, and compare against pure execution, supervised fine-tuning, and test-time-scaling baselines such as verifier-free selection and voting. Our results suggest that skill-and-harness optimization is a practical route to self-evolving embodied agents when model training is expensive, unavailable, or undesirable.
