feat: lerobot integration

.github/workflows/pipeline.yaml

@@ -21,8 +21,10 @@ jobs:
         with:
           python-version: "3.10"
           cache: "pip"
+      - name: Install library
+        run: python -m pip install -e .
       - name: Install linting and formatting dependencies
-        run: python -m pip install '.[dev]'
+        run: python -m pip install 'pip>=25.1' && python -m pip install --group dev
       - name: Check formatting
         run: make checkformat
       # - name: Check linting

README.md

@@ -31,6 +31,7 @@ On top of vlagents you can then install a simulation environment where the agent
 We currently the following environments:
 - [maniskill](https://github.com/haosulab/ManiSkill)
 - [robot control stack](https://github.com/RobotControlStack/robot-control-stack)
+- [duobench](https://github.com/RobotControlStack/duobench)
 - [libero](https://github.com/Lifelong-Robot-Learning/LIBERO)
 
 
@@ -41,6 +42,14 @@ We currently support the following policies:
 - [openpi](https://github.com/Physical-Intelligence/openpi)
 - [vjepa2-ac](https://github.com/facebookresearch/vjepa2)
 - [diffusion policy](https://github.com/real-stanford/diffusion_policy)
+- [lerobot policies](https://github.com/huggingface/lerobot/tree/main/src/lerobot/policies)
+
+
+### LeRobot
+```shell
+pip install 'lerobot[all]'
+```
+
 
 
 ### Octo
@@ -74,7 +83,7 @@ pip install git+https://github.com/juelg/vlagents.git
 For more details, see the [Octo github page](https://github.com/octo-models/octo).
 
 #### Troubleshooting
-If pip conplains about dependency issues than it might have happened that torch somehow slipped in.
+If pip complains about dependency issues than it might have happened that torch somehow slipped in.
 Check if you have any torch packages installed by
 ```shell
 pip freeze | grep torch
@@ -152,6 +161,16 @@ Currently located on the branch `diffusion_policy`.
 ## Usage
 To start an vlagents server use the `start-server` command where `kwargs` is a dictionary of the constructor arguments of the policy you want to start e.g.
 ```shell
+# lerobot act (n_action_steps is the executed horizon of the action chunk)
+python -m vlagents start-server lerobot --port 8080 --host 0.0.0.0 --kwargs '{"policy_name": "act", "checkpoint_path": "<path to pretrained_model>", "n_action_steps": 1}'
+
+# lerobot pi05
+python -m vlagents start-server lerobot --port 20000 --host 0.0.0.0 --kwargs '{"policy_name": "pi05", "checkpoint_path": "<path to pretrained_model>", "n_action_steps": 1}'
+
+# lerobot xvla
+uv run python -m vlagents start-server lerobot --port 20000 --host 0.0.0.0 --kwargs '{"policy_name": "xvla", "checkpoint_path": "<path to pretrained_model>", "n_action_steps": 1, "rename_map": {"head": "image", "left_wrist": "image2", "right_wrist": "image3"}}'
+
+
 # octo
 python -m vlagents start-server octo --host localhost --port 8080 --kwargs '{"checkpoint_path": "hf://Juelg/octo-base-1.5-finetuned-maniskill", "checkpoint_step": None, "horizon": 1, "unnorm_key": []}'
 
@@ -245,7 +264,8 @@ In order to extend the library with a new agent environment, extend the `Evaluat
 ### Developer Tools
 Install the following dev dependencies:
 ```shell
-pip install -ve '.[dev]'
+pip install 'pip>=25.1'
+pip install --group dev
 ```
 
 The following dev tools are provided:
@@ -261,12 +281,18 @@ make test
 ```
 
 ## Citation
-If you find the agent useful for your work, please consider citing the original work behind it:
+If you find the agent useful for your work, please consider citing the original works behind it:
 ```
 @inproceedings{juelg2025refinedpolicydistillationvla,
     title={{Refined Policy Distillation}: {F}rom {VLA} Generalists to {RL} Experts}, 
     author={Tobias J{\"u}lg and Wolfram Burgard and Florian Walter},
     year={2025},
-    booktitle={Proc.~of the IEEE/RSJ Int.~Conf.~on Intelligent Robots and Systems (IROS)},
+    booktitle={Proc.~of the IEEE/RSJ Int.~Conf.~on Intelligent Robots and Systems (IROS)}
+}
+@misc{juelg2026vlagentspolicyserverefficient,
+      title={VLAgents: A Policy Server for Efficient VLA Inference}, 
+      author={Tobias J{\"u}lg and Khaled Gamal and Nisarga Nilavadi and Pierre Krack and Seongjin Bien and Michael Krawez and Florian Walter and Wolfram Burgard},
+      year={2026},
+      howpublished={\url{https://arxiv.org/abs/2601.11250}}
 }
 ```

pyproject.toml

@@ -33,7 +33,7 @@ Homepage = "https://github.com/RobotControlStack/vlagents"
 Issues = "https://github.com/RobotControlStack/vlagents/issues"
 
 
-[project.optional-dependencies]
+[dependency-groups]
 dev = [
   "ruff",
   "black",

src/tests/test_eval_merge.py

@@ -0,0 +1,36 @@
+import numpy as np
+
+from vlagents.__main__ import _merge_env_split_results
+from vlagents.evaluator_envs import EvalConfig
+
+
+def test_merge_env_split_results_keeps_distinct_seeded_cfgs():
+    cfg_a = EvalConfig("duobench/spring_door", {}, max_steps_per_episode=900, seed=0, jpeg_encoding=True)
+    cfg_b = EvalConfig("duobench/spring_door", {}, max_steps_per_episode=900, seed=10, jpeg_encoding=True)
+
+    results = [
+        (
+            np.array([[[1.0, 0.1, 100.0], [0.0, 0.2, 200.0]]]),
+            [[[0.1], [0.2]]],
+            [0.15],
+            40000,
+        ),
+        (
+            np.array([[[0.0, 0.3, 300.0], [1.0, 0.4, 400.0]]]),
+            [[[0.3], [0.4]]],
+            [0.35],
+            40000,
+        ),
+    ]
+
+    merged_last_reward, merged_rewards, merged_mean_rewards, merged_step = _merge_env_split_results(
+        results=results,
+        worker_eval_cfgs=[[cfg_a], [cfg_b]],
+        eval_cfgs=[cfg_a, cfg_b],
+    )
+
+    assert merged_step == 40000
+    assert np.array_equal(merged_last_reward[0], results[0][0][0])
+    assert np.array_equal(merged_last_reward[1], results[1][0][0])
+    assert merged_rewards == [results[0][1][0], results[1][1][0]]
+    assert merged_mean_rewards == [0.15, 0.35]

src/tests/test_libero.py

@@ -3,9 +3,9 @@
     import os
 
     import numpy as np
+    from lerobot.envs.libero import LiberoEnv
     from PIL import Image
 
-    from lerobot.envs.libero import LiberoEnv
     from vlagents.__main__ import _run_eval
     from vlagents.evaluator_envs import AgentConfig, EvalConfig