Einsia / EmbodiedEvo

# EmbodiedEvo: Self-Evolving Coding Agent For Physical Intelligence
**Observe, revise code, and act. Learn Skills and Lessons from physical feedback.**
EmbodiedEvo connects a coding agent's workspace to a persistent RoboDojo simulation
session. The agent inspects observations, edits Python control code, executes it,
and uses the resulting physical feedback to guide its next revision. Its evolving
knowledge lives in reusable code and written lessons in the workspace.
1. **Observe, revise, execute.** Inspect camera observations and robot state,
 update control code, and execute the revised segment in the same Python and
 simulation session. New observations close the interaction loop.
2. **Build Skills and Lessons in Playground.** Turn interaction experience into
 reusable helpers in `skills/` and practical guidance in `lessons/`, such as
 grasping from above, verifying a grasp, or carrying an object in small steps.
 Playground permits same-scene resets while the agent refines this knowledge.
3. **Freeze and reuse in Test.** Freeze only `skills/` and `lessons/` before
 selecting Test scenes. Each scene starts with a fresh agent, workspace, and
 simulator. The transferred knowledge is read-only; the agent can observe and
 revise code within the scene, but cannot reset or learn across Test scenes.
## Setup
Use a Linux host and Python 3.11 or newer for the orchestration environment:
 [code] 
Build the thin coding-agent image directly from the included Dockerfile:
 [code] 
The Dockerfile installs pinned Codex CLI 0.159.2 and basic Python/image tools;
no host Codex installation or API key is needed for the build. Its context
contains no credentials, task files or simulator assets. Build
the image on each Docker host that will run agents; the simulator is provisioned
separately. See the complete image build procedure.
Run commands from this repository root. The editable installation keeps task
templates and recipes available at their source paths. The project is currently
distributed as a source checkout, not a standalone wheel with simulator assets.
Real episodes require a separately provisioned RoboDojo/Isaac Sim environment,
assets, an available NVIDIA GPU, Docker, and configured model credentials.
Follow installation, including
Codex API configuration, before starting an experiment.
## Playground and Test
After setup, choose a native task with `--env-task`. The model and reasoning
effort are explicit in `tasks/RoboDojo/task.toml`; configure them before a new run.
 [code] 
`embodiedevo run` performs both stages. Test-only baseline:
 [code] 
The baseline starts with its own instruction and no transferred Skills/Lessons.
Public task recipes are enabled by default for both configurations, matching the
source snapshot. Set `EMBODIEDEVO_USE_RECIPE=0` before preparing a new run to omit
that supplied knowledge. The choice and recipe source are recorded per run.
See the protocol and running and evaluation.
## Source map
| Path | Role |
| --- | --- |
| `orches