The solution operates as an intermediary layer between the robot's hardware and its AI algorithms.

It employs an energy management AI that constantly monitors the robot's power reserves and adjusts the computational intensity of AI tasks accordingly.

This allows the system to dynamically scale down algorithm complexity during low-energy scenarios or when full performance is not needed, thus conserving power.

Additionally, the middleware supports lightweight AI models optimized for energy-efficient operations using techniques like pruning, quantization, and knowledge distillation.

The middleware leverages existing AI compression and optimization techniques, so it's feasible with contemporary technologies.

Initial development costs could be significant due to the need for custom compatibility with various robotic platforms, but partnerships with key hardware manufacturers can mitigate this.

As AI model optimization techniques are rapidly advancing, this proposed solution is well-aligned with technical and market trends.