Physical Agents
From Copilots to AGI Robots: The Reborn Evolution
Reborn is not just building models for robots—it is building intelligent physical agents that evolve in capability over time. These agents progress through three distinct phases: copilot/teleoperation, specialized autonomy, and generalized intelligence. Together, they form a practical roadmap toward scalable embodied AI, backed by real-world deployment and continuous data-model feedback loops.
Phase 1: Copilot / Teleoperation
The journey begins with human-in-the-loop control. In this stage, robots are operated remotely by human workers using high-fidelity teleoperation systems developed by Reborn. These systems support:
Low-latency, high-precision motion streaming,
Mixed reality interfaces, enabling intuitive human control over robotic limbs,
And force-aware telepresence, allowing operators to respond to physical feedback in real time.
This phase serves three critical functions:
Commercial Utility: It enables underemployed populations in developing regions to remotely operate robots in developed cities—for example, a worker in the Philippines controlling a robot that serves customers in a Los Angeles restaurant.
Data Generation: Every teleoperated action generates labeled human-robot interaction data across various environments—fuel for downstream model training.
Task Familiarization: It allows Reborn to identify recurring task patterns, motion primitives, and embodiment-specific constraints that will later be distilled into autonomous models.
Phase 2: Specialized Model
With sufficient interaction data and operational insight, Reborn transitions from teleoperated agents to specialized, partially or fully autonomous robots trained for specific tasks. These robots operate in vertical domains such as:
Hospitality (e.g., robotic waiters with gesture-based serving policies)
Manufacturing (e.g., robotic arms trained to fasten screws on car chassis)
Logistics and warehouse automation
Healthcare and caregiving
These models are designed to be easily deployable through the Reborn Physical AI App Store, allowing users—whether they are developers, robotics startups, or hardware manufacturers—to install advanced capabilities onto supported robots with just a few clicks. This streamlines the traditionally complex process of integrating AI into physical machines, reducing both technical overhead and deployment time. Once deployed, the models are continually improved through Reborn’s closed-loop data infrastructure: each interaction generates new real-world data, which is fed back into the system to refine and retrain models, enabling them to adapt to new edge cases and evolving usage patterns. Furthermore, the models are built with modularity and abstraction in mind, allowing them to be transferred across different robotic platforms with minimal modification. Whether the robot is a humanoid waiter, a warehouse assistant, or a factory arm, Reborn’s models can be retargeted and reused across embodiments by separating task logic from hardware-specific actuation—maximizing generalization and lowering the barrier to robot intelligence at scale.
🌐 Phase 3: Generalized Model
The final vision of Reborn is to create a general-purpose embodied agent—an AGI-level robot capable of adapting across tasks, environments, and robot embodiments with minimal retraining. These generalized models are designed to reason over multi-modal inputs such as vision, language, and proprioception, execute open-vocabulary tasks with physical actions, and transfer learned behaviors across robots with different body structures. They continuously improve through deployment and feedback, enabling lifelong learning in the real world. This new class of models combines foundation-level Vision-Language-Action reasoning, dynamic world modeling across diverse scenarios, and cross-embodiment policy adaptation—all made possible by Reborn’s expansive real-world data corpus and simulation-aligned training workflows.
Over time, these generalized agents will blur the line between individual robot skillsets, merging all vertical applications into a cohesive, autonomous, physical AI system.
Last updated