Today's autonomous agents can understand free-form natural language instructions and execute long-horizon tasks in a manner akin to human-level reasoning. These capabilities are mostly driven by large-scale pre-trained foundation models (FMs). However, the approaches with which these models are grounded for human-robot interaction (HRI) perpetuate a master-apprentice model, where the apprentice (embodied agent) passively receives and executes the master's (human's) commands without reciprocal learning. This reactive interaction approach does not capture the co-adaptive dynamics inherent in everyday multi-turn human-human interactions. To address this, we propose a Symbiotic Interactive Learning (SIL) approach that enables both the master and the apprentice to co-adapt through mutual, bidirectional interactions. We formalised SIL as a co-adaptation process within a shared latent task space, where the agent and human maintain joint belief states that evolve based on interaction history. This enables the agent to move beyond reactive execution to proactive clarification, adaptive suggestions, and shared plan refinement. To realise these novel behaviours, we leveraged pre-trained FMs for spatial perception and reasoning, alongside a lightweight latent encoder that grounds the models' outputs into task-specific representations. Furthermore, to ensure stability as the tasks evolve, we augment SIL with a memory architecture that prevents the forgetting of learned task-space representations. We validate SIL on both simulated and real-world embodied tasks, including instruction following, information retrieval, query-oriented reasoning, and interactive dialogues. Demos and resources are public at:~\href{https://linusnep.github.io/SIL/}{https://linusnep.github.io/SIL/}.

翻译：当今的自主智能体能够理解自由形式的自然语言指令，并以类人类推理的方式执行长时程任务。这些能力主要由大规模预训练基础模型驱动。然而，这些模型在人机交互中的实现方式仍延续着主从模式，即从属方（具身智能体）被动接收并执行主导方（人类）的指令，缺乏双向学习机制。这种被动交互方式无法捕捉日常多轮人-人交互中固有的协同适应动态。为此，我们提出一种共生交互学习方法，使主导方与从属方能够通过双向交互实现协同适应。我们将SIL形式化为共享潜在任务空间中的协同适应过程，其中智能体与人类保持基于交互历史演化的联合信念状态。这使得智能体能够超越被动执行，实现主动澄清、适应性建议和协同计划优化。为实现这些新型行为，我们利用预训练基础模型进行空间感知与推理，并辅以轻量级潜在编码器将模型输出映射为任务特定表征。此外，为确保任务演化过程中的稳定性，我们为SIL增加了记忆架构以防止已学习任务空间表征的遗忘。我们在仿真与真实世界具身任务中验证了SIL的有效性，包括指令跟随、信息检索、查询导向推理和交互对话。演示与资源公开于：https://linusnep.github.io/SIL/。