Language-Driven Autonomy for Sustainable Consumer Robotics: Toward Energy- and Data-Efficient LLM Reasoning

Large Language Models (LLMs) are rapidly being embedded in consumer and service robots, enabling richer human-robot interaction, multimodal reasoning, and language-driven autonomy. However, the computational and lifecycle costs of training, inference, and continuous upgrade cycles raise urgent digital sustainability concerns: energy consumption, network dependency, privacy exposure, and hardware obsolescence. In this conceptual paper, we introduce the Sustainable Language-Driven Autonomy Framework (SLAF), a modular architecture and set of operational policies that align multimodal LLM reasoning with sustainability goals. SLAF decomposes the intelligence stack into Perception & Preprocessing, Local Cognition (Edge), High-level Reasoning (LLM), and Control & Execution layers, mediated by an Adapter responsible for compact semantic encoding, adaptive triggers, caching, and energy budgets. We propose quantitative primitives and trade-off models (e.g., energy-per-inference E_inf, calls-per-mission N_calls, mission energy E_mission) and an evaluation protocol to make sustainability claims comparable and auditable. Finally, we map how SLAF addresses four research questions on zero-shot generalization, energy-efficient architectures, software-first lifespan extension, and cloud/on-device trade-offs. We conclude with a roadmap for empirical validation, lifecycle analysis, and user-centered studies to operationalize sustainable, language-enabled robotics.