Eden: One Task Definition from Simulation to Real Robots

Simulation has become central to robot learning, yet a task is often reimplemented as it moves from training to data collection, evaluation, and real-world deployment. In particular, deploying a policy on a physical robot typically requires reconstructing its observation and action pipelines against a new hardware interface, creating duplicated code and opportunities for subtle inconsistencies. We present Eden, a robot-learning framework in which a task is defined once and executed across simulation and hardware. Eden represents each task as a typed, serializable configuration of reusable terms operating over name-resolved entities. This separation of task semantics from the underlying execution substrate allows a simulated robot to be replaced by a hardware-backed entity while preserving the observation, action, and command code used during training. The same task definition also supports data collection and evaluation under alternative physics without reimplementation. We test whether this abstraction yields measurable benefits. In a controlled agentic benchmark across Eden, IsaacLab, and mjlab, Eden reduces the effort required by coding agents to construct novel tasks involving new scenes and component composition, despite substantially less prior exposure to Eden code. We show that policies trained in simulation transfer to hardware such as the Unitree G1 and LimX Oli without rewriting their observation or action pipelines. Separately, a vision-language-action policy fine-tuned on only 100 simulated demonstrations collected in Eden succeeds in 27/30 real-world pick-and-place trials. Together, these results show that training, data collection, evaluation, and deployment can be different executions of a single robot-learning task definition rather than separate implementations.