EmbodiedSWE: Coding Agents for Long-Horizon Dexterous Robotics
EmbodiedSWE studies how frontier coding agents can help robotics. It has four parts:
- EmbodiedSWE-Bench, an agent-native benchmark of long-horizon, dexterous everyday tasks, built on Isaac Lab.
- Evaluation of frontier coding agents on these tasks: task performance, completion time, and inference cost.
- EmbodiedSWE-Gen, which diversifies one verified agent solution into a large trajectory dataset for training general robot policies.
- Agent improvement, which generates new tasks from existing ones and improves the coding agent with RL on verified outcomes.
See the paper for the full benchmark, evaluation, and data-generation details.
Note: this repository is under active development. Folder layouts, interfaces, and settings may change as the codebase evolves.
Installation
Requirements: Linux, an NVIDIA GPU with a CUDA 12.x driver, and uv.
git clone <this repo> && cd <this repo>
./scripts/bootstrap_isaaclab_5_1.sh # Isaac Sim 5.1 + Isaac Lab 2.3.2 + robobench into ./.venv
source .venv/bin/activate
export OMNI_KIT_ACCEPT_EULA=YES # Isaac Sim asks interactively otherwise (hangs headless runs)
# Optional: prefetch every task and room (~3.4 GB); otherwise env.build() fetches what it needs.
python -m robobench.scripts.fetch_assets
uv pip install "pin==2.7.0" "pin-pink==3.1.0" "daqp==0.8.5" "numpy==1.26.0" # whole-body IK (pink_ik)
Task assets and shared rooms live in the Hugging Face dataset EmbodiedSWE/robobench-assets.
Building an environment automatically downloads its required asset groups and verifies their SHA-256
checksums against robobench/assets_manifest.json. Verified local copies are reused, including offline.
Listing tasks does not download anything. fetch_assets --check verifies the whole local collection.
Every built-in registered task configuration declares its default room in its suite's
configs/envs.py, including alternate robots and control modes. Shared loading code lives in
robobench/core/rooms.py; downloaded rooms live in the ignored robobench/assets/rooms/ directory.
Use --room none with the smoke launcher or cfg.build(room=None) to disable scenery explicitly.
Set COSIGEN_ASSET_DIR before starting Python to use a writable cache outside the checkout.
See task rooms and asset downloads for the room assignments and maintainer commands.
The deformable suite runs on the Newton physics backend and needs a separate venv; see
robobench/suites/deformable/README.md.
Quick start
Preview a task:
python -m robobench.scripts.smoke --list # all registered tasks
python -m robobench.scripts.smoke --env assembly.bulb.franka.osc --livestream 2 # random actions, live view
Tasks are named suite.scene[.robot[.control_mode]]. The environment API and design are described
in robobench/README.md.
A few examples from EmbodiedSWE-Bench:
 assembly.bulb |
 assembly.ikea_table |
 assembly.so101 |
 assembly.pc_motherboard |
 packing.tool_packing |
 packing.egg_carton |
 deformable.tshirt |
 deformable.latte |
 cutting.slice |
 locomanip.fruit_delivery |
Solving a task
The easy way: open the repo in a coding agent such as Claude Code or Codex, point it at
robobench/README.md, and ask it to solve a task, e.g. assembly.bulb.franka.osc. A solution is a
Python program exposing solve(env); the contract the agents see is
eval/prompts/_contract.md.
For rigorous, large-scale evaluation, eval/ runs the agent in an isolated Docker container under a
time budget and grades its solutions afterwards in fresh containers on independently randomized episodes.
python eval/scripts/build_env.py --name bulb_e2e --stage bulb:franka # build the world once
python eval/scripts/run_agent.py experiments/bulb_e2e --agent claude # one agent run in Docker
python eval/scripts/run_grade.py experiments/bulb_e2e --run <run> # grade the delivery
Prompt conditions (hints, rules, blocked features) are authored yaml files in eval/configs/ and
eval/prompts/. Docker images and the container contract are documented in
eval/docker/README.md.
Data engine
Once a task is solved, data_engine/ turns that one verified solution into a large, diverse,
per-episode-verified demonstration dataset. Coding agents author the diversity at five independent
levels: scene, strategy, phase, dynamics, and visual.
A generic launcher then mass-produces batched episodes, the scene's grader stamps a verdict on each one, and the verified episodes are rendered and baked into a LeRobot dataset for policy training.
python data_engine/scripts/init_gen.py experiments/bulb_e2e/runs/<run> # start a campaign from a solved run
python data_engine/scripts/diversify.py <gen_root> data_engine/configs/scene_default.yaml # agent session that adds diversity
python data_engine/scripts/generate.py --headless <gen_root> --scene scene_1 --num_envs 8 # generate + verify a batch
python data_engine/scripts/render.py --headless <gen_root> --batches <batch> # render episodes to video
.venv-lerobot/bin/python vla/convert/convert.py <gen_root> --repo-id <name> # bake a LeRobot dataset
Training and closed-loop evaluation of VLA policies live in vla/.
Repository layout
robobench/ EmbodiedSWE-Bench: the benchmark package
core/ BaseEnv, BaseScene, BaseRobot, controller and grader contracts, registries
suites/ task suites: assembly, packing, puzzle, deformable, cutting, locomanip
robots/ embodiments: franka, xarm7, attached (Kinova Gen3 + panda hand), g1, multi (bimanual), ...
controllers/ joint, diff_ik, task_space (OSC / impedance), pink_ik, composite, loco_policy
scripts/smoke.py build and step any registered env
eval/ dockerized agent evaluation
data_engine/ EmbodiedSWE-Gen: expands one solution into diverse trajectories
vla/ bake episodes into LeRobot datasets, train and evaluate VLA policies
rl/ RL baselines (not agent improvement)
sim_gen/ generates new simulation tasks from seed tasks (agent improvement)
real_to_sim/ real scenes and objects to sim: splat backgrounds, photos to sim-ready assets
scripts/ bootstrap installers, record_video.py, asset vendoring
Citation
If you use EmbodiedSWE in your research, please cite our paper:
@misc{embodiedswe2026,
title = {EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics},
author = {Shen, Zeyu and You, Haoxiang and Liu, Yilang and Zheng, Zhicheng and Zha, Lihan and
Yamazaki, Kashu and Zhang, Mingtong and Huang, Suning and Sun, Jiankai and
Chen, Qianzhong and He, Lucy and Liu, Kaiyuan and Chang, Haoran and Fragkiadaki, Katerina and
Shah, Dhruv and Schwager, Mac and Henderson, Peter and Abraham, Ian and Xu, Canwen},
year = {2026},
eprint = {2609.27308},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.27308},
}
License
Apache 2.0. See LICENSE.