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Robix: A Unified Model for Robot Interaction, Reasoning, and Planning

Researchers have introduced Robix, a novel unified model designed to equip robots with advanced capabilities for interacting with humans, planning complex tasks, and reasoning about their environment. Presented as the high-level cognitive layer in a robot system, Robix aims to enable robots to understand intricate instructions, execute multi-step tasks, and engage in natural dialogue with people.

Unlike previous approaches that often break down robot tasks into rigid, modular pipelines, Robix employs a single, end-to-end vision-language architecture. This allows it to seamlessly integrate perception, reasoning, planning, and natural language interaction. At its core, Robix utilizes a “chain-of-thought” reasoning process, which essentially means it breaks down complex problems into intermediate steps, much like a human would think through a problem.

The development of Robix involved a three-stage training strategy. First, it underwent continued pretraining to bolster its foundational “embodied reasoning” abilities. This includes understanding 3D spatial relationships, grounding language to visual elements, and reasoning about tasks within a physical context. For example, imagine a robot needing to place specific items on a shelf. Embodied reasoning would allow it to understand not just the word “shelf” but also its spatial constraints, the size and shape of the objects, and how they should be arranged.

Second, Robix was fine-tuned with supervised learning to model human-robot interactions and task planning as a unified reasoning-action sequence. This stage synthesized data to teach Robix how to handle complex instructions, plan long-term tasks, monitor progress, and even deal with real-time interruptions. A concrete example would be a robot cleaning a table; it might need to distinguish between plates that need clearing and those that should remain, a task requiring nuanced understanding and adaptive planning.

Finally, reinforcement learning was employed to further refine Robix’s reasoning abilities and ensure its actions consistently align with its thoughts, especially in long-horizon, interactive tasks. This helps the robot learn from its experiences and improve its decision-making over time.

Experiments conducted by the research team demonstrate that Robix significantly outperforms existing open-source and commercial baselines, including models like GPT-40 and Gemini 2.5 Pro, in interactive task execution. It shows strong generalization capabilities across a variety of instruction types, from open-ended requests to complex, multi-stage tasks, and even handles interrupted commands effectively. The research highlights Robix’s success in user-involved tasks such as table bussing, grocery shopping, and dietary filtering, showcasing its potential for creating more intelligent and adaptable robots.