openvla policy intergration

.gitmodules

@@ -1,3 +1,4 @@
 [submodule "ManiSkill2_real2sim"]
 	path = ManiSkill2_real2sim
-	url = https://github.com/simpler-env/ManiSkill2_real2sim
+	url = https://github.com/allenzren/ManiSkill2_real2sim.git
+	# url = https://github.com/simpler-env/ManiSkill2_real2sim

README.md

@@ -1,4 +1,4 @@
-# SimplerEnv: Simulated Manipulation Policy Evaluation Environments for Real Robot Setups
+# SimplerEnv: Simulated Manipulation Policy Evaluation Environments for Real Robot Setups (Multi-model Support 🔥)
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/simpler-env/SimplerEnv/blob/main/example.ipynb)
 
@@ -23,12 +23,45 @@ We hope that our work guides and inspires future real-to-sim evaluation efforts.
   - [Code Structure](#code-structure)
   - [Adding New Policies](#adding-new-policies)
   - [Adding New Real-to-Sim Evaluation Environments and Robots](#adding-new-real-to-sim-evaluation-environments-and-robots)
-  - [Full Installation (RT-1 and Octo Inference, Env Building)](#full-installation-rt-1-and-octo-inference-env-building)
+  - [Full Installation (RT-1, Octo, OpenVLA Inference, Env Building)](#full-installation-rt-1-octo-openvla-inference-env-building)
     - [RT-1 Inference Setup](#rt-1-inference-setup)
     - [Octo Inference Setup](#octo-inference-setup)
+    - [OpenVLA Inference Setup](#openvla-inference-setup)
   - [Troubleshooting](#troubleshooting)
   - [Citation](#citation)
 
+## Benchmark @ GoogleSheets
+> [!TIP]
+> We maintain a public Google Sheets documenting the latest SOTA models' performance and fine-tuned weights on Simpler-Env, making community benchmarking more accessible. Welcome to contribute and update!
+>
+> [simpler env benchmark @ GoogleSheets 📊](https://docs.google.com/spreadsheets/d/1cLhEW9QnVkP4rqxsFVzdBVRyBWVdSm0d5zp1L_-QJx4/edit?usp=sharing)
+<img width="1789" alt="image" src="https://github.com/user-attachments/assets/68e2ad3d-b24f-4562-97d9-434f23cede86" />
+
+
+
+## Models
+> [!NOTE]
+> Hello everyone!
+> This repository has now fully opened issues and discussions. We warmly welcome you to: 🤗
+> Discuss any problems you encounter 🙋
+> Submit fixes ✅
+> Support new models! 🚀
+> Given the significant environmental differences across various models and the specific dependencies required for simulator rendering, I will soon provide a Docker solution and a benchmark performance table. I’ll also do my best to address any issues you run into.
+> Thank you for your support and contributions! 🎉
+>
+> To support state input, we use the submodule `ManiSkill2_real2sim` from https://github.com/allenzren/ManiSkill2_real2sim
+
+| Model Name   | support | Note  |
+| -----------  | -----  | -----  |
+| Octo         | ✅     |        |
+| RT1          | ✅     |        |
+| OpenVLA      | ✅     |        |
+| CogACT       | ✅     | OpenVLA-based         |
+| SpatialVLA   | ✅     | [transformers == 4.47.0](https://github.com/SpatialVLA/SpatialVLA) |
+| Pi0/Pi0-Fast (openpi version) | ✅     | [openpi](https://github.com/Physical-Intelligence/openpi) |
+| Pi0/Pi0-Fast (lerobot version)     | ✅ | [lerobot](https://github.com/huggingface/lerobot) |
+| GR00T     | ✅ | [Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T) |
+
 
 ## Getting Started
 
@@ -77,7 +110,7 @@ conda activate simpler_env
 
 Clone this repo:
 ```
-git clone https://github.com/simpler-env/SimplerEnv --recurse-submodules
+git clone https://github.com/simpler-env/SimplerEnv --recurse-submodules --depth 1
 ```
 
 Install numpy<2.0 (otherwise errors in IK might occur in pinocchio):
@@ -97,15 +130,15 @@ cd {this_repo}
 pip install -e .
 ```
 
-**If you'd like to perform evaluations on our provided agents (e.g., RT-1, Octo), or add new robots and environments, please additionally follow the full installation instructions [here](#full-installation-rt-1-and-octo-inference-env-building).**
+**If you'd like to perform evaluations on our provided agents (e.g., RT-1, Octo, OpenVLA), or add new robots and environments, please additionally follow the full installation instructions [here](#full-installation-rt-1-octo-openvla-inference-env-building).**
 
 
 ## Examples
 
-- Simple RT-1 and Octo evaluation script on prepackaged environments with visual matching evaluation setup: see [`simpler_env/simple_inference_visual_matching_prepackaged_envs.py`](https://github.com/simpler-env/SimplerEnv/blob/main/simpler_env/simple_inference_visual_matching_prepackaged_envs.py).
+- Simple RT-1, Octo, and OpenVLA evaluation script on prepackaged environments with visual matching evaluation setup: see [`simpler_env/simple_inference_visual_matching_prepackaged_envs.py`](https://github.com/simpler-env/SimplerEnv/blob/main/simpler_env/simple_inference_visual_matching_prepackaged_envs.py).
 - Colab notebook for RT-1 and Octo inference: see [this link](https://colab.research.google.com/github/simpler-env/SimplerEnv/blob/main/example.ipynb).
 - Environment interactive visualization and manual control: see [`ManiSkill2_real2sim/mani_skill2_real2sim/examples/demo_manual_control_custom_envs.py`](https://github.com/simpler-env/ManiSkill2_real2sim/blob/main/mani_skill2_real2sim/examples/demo_manual_control_custom_envs.py)
-- Policy inference scripts to reproduce our Google Robot and WidowX real-to-sim evaluation results with sweeps over object / robot poses and advanced loggings. These contain both visual matching and variant aggregation evaluation setups along with RT-1, RT-1-X, and Octo policies. See [`scripts/`](https://github.com/simpler-env/SimplerEnv/tree/main/scripts).
+- Policy inference scripts to reproduce our Google Robot and WidowX real-to-sim evaluation results with sweeps over object / robot poses and advanced loggings. These contain both visual matching and variant aggregation evaluation setups along with RT-1, RT-1-X, Octo, and OpenVLA policies. See [`scripts/`](https://github.com/simpler-env/SimplerEnv/tree/main/scripts).
 - Real-to-sim evaluation videos from running `scripts/*.sh`: see [this link](https://huggingface.co/datasets/xuanlinli17/simpler-env-eval-example-videos/tree/main).
 
 ## Current Environments
@@ -183,6 +216,7 @@ simpler_env/
    policies/: policy implementations
       rt1/: RT-1 policy implementation
       octo/: Octo policy implementation
+      openvla/: OpenVLA policy implementation
    utils/:
       env/: environment building and observation utilities
       debug/: debugging tools for policies and robots
@@ -205,7 +239,7 @@ scripts/: example bash scripts for policy inference under our variant aggregatio
 
 If you want to use existing environments for evaluating new policies, you can keep `./ManiSkill2_real2sim` as is.
 
-1. Implement new policy inference scripts in `simpler_env/policies/{your_new_policy}`, following the examples for RT-1 (`simpler_env/policies/rt1`) and Octo (`simpler_env/policies/octo`) policies.
+1. Implement new policy inference scripts in `simpler_env/policies/{your_new_policy}`, following the examples for RT-1 (`simpler_env/policies/rt1`), Octo (`simpler_env/policies/octo`), and OpenVLA (`simpler_env/policies/openvla`) policies.
 2. You can now use `simpler_env/simple_inference_visual_matching_prepackaged_envs.py` to perform policy evaluations in simulation.
    - If the policy behaviors deviate a lot from those in the real-world, you can write similar scripts as in `simpler_env/utils/debug/{policy_name}_inference_real_video.py` to debug the policy behaviors. The debugging script performs policy inference by feeding real eval video frames into the policy. If the policy behavior still deviates significantly from real, this may suggest that policy actions are processed incorrectly into the simulation environments. Please double check action orderings and action spaces.
 3. If you'd like to perform customized evaluations,
@@ -219,7 +253,7 @@ If you want to use existing environments for evaluating new policies, you can ke
 We provide a step-by-step guide to add new real-to-sim evaluation environments and robots in [this README](ADDING_NEW_ENVS_ROBOTS.md)
 
 
-## Full Installation (RT-1 and Octo Inference, Env Building)
+## Full Installation (RT-1, Octo, OpenVLA Inference, Env Building)
 
 If you'd like to perform evaluations on our provided agents (e.g., RT-1, Octo), or add new robots and environments, please follow the full installation instructions below.
 
@@ -289,6 +323,13 @@ If you are using CUDA 12, then to use GPU for Octo inference, you need CUDA vers
 
 `PATH=/usr/local/cuda-12.3/bin:$PATH   LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64:$LD_LIBRARY_PATH   bash scripts/octo_xxx_script.sh`
 
+### OpenVLA Inference Setup
+
+```
+pip install torch==2.3.1 torchvision==0.18.1 timm==0.9.10 tokenizers==0.15.2 accelerate==0.32.1
+pip install flash-attn==2.6.1 --no-build-isolation
+```
+
 ## Troubleshooting
 
 1. If you encounter issues such as
@@ -307,6 +348,10 @@ Follow [this link](https://maniskill.readthedocs.io/en/latest/user_guide/getting
 TypeError: 'NoneType' object is not subscriptable
 ```
 
+3. Please also refer to the original repo or [vulkan_setup](https://github.com/SpatialVLA/SpatialVLA/issues/3#issuecomment-2641739404) if you encounter any problems.
+
+4. `tensorflow-2.15.0` conflicts with `tensorflow-2.15.1`?
+The dlimp library has not been maintained for a long time, so the TensorFlow version might be out of date. A reliable solution is to comment out tensorflow==2.15.0 in the requirements file, install all other dependencies, and then install tensorflow==2.15.0 finally. Currently, using tensorflow==2.15.0 has not caused any problems.
 
 ## Citation
 

docker/Dockerfile

@@ -0,0 +1,24 @@
+FROM qudelin/simpler-env
+
+# Configure environment variables
+ARG PYTHON_VERSION=3.10
+ENV DEBIAN_FRONTEND=noninteractive
+ENV MUJOCO_GL="egl"
+# ENV PATH="/opt/venv/bin:$PATH"
+
+# Install dependencies and set up Python in a single layer
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential cmake git \
+    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+    # speech-dispatcher libgeos-dev \
+    # python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
+    # && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
+    # && python -m venv /opt/venv \
+    # && apt-get clean && rm -rf /var/lib/apt/lists/* \
+    # && echo "source /opt/venv/bin/activate" >> /root/.bashrc
+
+# Clone repository and install LeRobot in a single layer
+# COPY . /lerobot
+# WORKDIR /lerobot
+# RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
+#     && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]"

scripts/bridge.sh

@@ -0,0 +1,52 @@
+ckpt_path=$1
+policy_model=$2
+action_ensemble_temp=$3
+logging_dir=$4
+gpu_id=$5
+
+
+scene_name=bridge_table_1_v1
+robot=widowx
+rgb_overlay_path=ManiSkill2_real2sim/data/real_inpainting/bridge_real_eval_1.png
+robot_init_x=0.147
+robot_init_y=0.028
+
+CUDA_VISIBLE_DEVICES=${gpu_id} python simpler_env/main_inference.py --policy-model ${policy_model} --ckpt-path ${ckpt_path} --action-ensemble-temp ${action_ensemble_temp} --logging-dir ${logging_dir} \
+  --robot ${robot} --policy-setup widowx_bridge \
+  --control-freq 5 --sim-freq 500 --max-episode-steps 60 \
+  --env-name PutCarrotOnPlateInScene-v0 --scene-name ${scene_name} \
+  --rgb-overlay-path ${rgb_overlay_path} \
+  --robot-init-x ${robot_init_x} ${robot_init_x} 1 --robot-init-y ${robot_init_y} ${robot_init_y} 1 --obj-variation-mode episode --obj-episode-range 0 24 \
+  --robot-init-rot-quat-center 0 0 0 1 --robot-init-rot-rpy-range 0 0 1 0 0 1 0 0 1;
+
+CUDA_VISIBLE_DEVICES=${gpu_id} python simpler_env/main_inference.py --policy-model ${policy_model} --ckpt-path ${ckpt_path} --action-ensemble-temp ${action_ensemble_temp} --logging-dir ${logging_dir} \
+  --robot ${robot} --policy-setup widowx_bridge \
+  --control-freq 5 --sim-freq 500 --max-episode-steps 60 \
+  --env-name StackGreenCubeOnYellowCubeBakedTexInScene-v0 --scene-name ${scene_name} \
+  --rgb-overlay-path ${rgb_overlay_path} \
+  --robot-init-x ${robot_init_x} ${robot_init_x} 1 --robot-init-y ${robot_init_y} ${robot_init_y} 1 --obj-variation-mode episode --obj-episode-range 0 24 \
+  --robot-init-rot-quat-center 0 0 0 1 --robot-init-rot-rpy-range 0 0 1 0 0 1 0 0 1;
+
+CUDA_VISIBLE_DEVICES=${gpu_id} python simpler_env/main_inference.py --policy-model ${policy_model} --ckpt-path ${ckpt_path} --action-ensemble-temp ${action_ensemble_temp} --logging-dir ${logging_dir} \
+  --robot ${robot} --policy-setup widowx_bridge \
+  --control-freq 5 --sim-freq 500 --max-episode-steps 60 \
+  --env-name PutSpoonOnTableClothInScene-v0 --scene-name ${scene_name} \
+  --rgb-overlay-path ${rgb_overlay_path} \
+  --robot-init-x ${robot_init_x} ${robot_init_x} 1 --robot-init-y ${robot_init_y} ${robot_init_y} 1 --obj-variation-mode episode --obj-episode-range 0 24 \
+  --robot-init-rot-quat-center 0 0 0 1 --robot-init-rot-rpy-range 0 0 1 0 0 1 0 0 1;
+
+
+scene_name=bridge_table_1_v2
+robot=widowx_sink_camera_setup
+rgb_overlay_path=ManiSkill2_real2sim/data/real_inpainting/bridge_sink.png
+robot_init_x=0.127
+robot_init_y=0.06
+
+CUDA_VISIBLE_DEVICES=${gpu_id} python simpler_env/main_inference.py --policy-model ${policy_model} --ckpt-path ${ckpt_path} --action-ensemble-temp ${action_ensemble_temp} --logging-dir ${logging_dir} \
+  --robot ${robot} --policy-setup widowx_bridge \
+  --control-freq 5 --sim-freq 500 --max-episode-steps 120 \
+  --env-name PutEggplantInBasketScene-v0 --scene-name ${scene_name} \
+  --rgb-overlay-path ${rgb_overlay_path} \
+  --robot-init-x ${robot_init_x} ${robot_init_x} 1 --robot-init-y ${robot_init_y} ${robot_init_y} 1 --obj-variation-mode episode --obj-episode-range 0 24 \
+  --robot-init-rot-quat-center 0 0 0 1 --robot-init-rot-rpy-range 0 0 1 0 0 1 0 0 1;
+

scripts/octo_drawer_variant_agg.sh → scripts/drawer_variant_agg.sh

@@ -1,10 +1,11 @@
 # shader_dir=rt means that we turn on ray-tracing rendering; this is quite crucial for the open / close drawer task as policies often rely on shadows to infer depth
+ckpt_path=$1
+policy_model=$2
+action_ensemble_temp=$3
+logging_dir=$4
+gpu_id=$5
 
-
-
-declare -a policy_models=(
-"octo-base"
-)
+declare -a ckpt_paths=(${ckpt_path})
 
 declare -a env_names=(
 OpenTopDrawerCustomInScene-v0
@@ -22,9 +23,9 @@ EXTRA_ARGS="--enable-raytracing"
 scene_name=frl_apartment_stage_simple
 
 EvalSim() {
-  echo ${policy_model} ${env_name}
+  echo ${ckpt_path} ${env_name}
 
-  python simpler_env/main_inference.py --policy-model ${policy_model} --ckpt-path None \
+  CUDA_VISIBLE_DEVICES=${gpu_id} python simpler_env/main_inference.py --policy-model ${policy_model} --ckpt-path ${ckpt_path} --action-ensemble-temp ${action_ensemble_temp} --logging-dir ${logging_dir} \
     --robot google_robot_static \
     --control-freq 3 --sim-freq 513 --max-episode-steps 113 \
     --env-name ${env_name} --scene-name ${scene_name} \
@@ -35,7 +36,7 @@ EvalSim() {
 }
 
 
-for policy_model in "${policy_models[@]}"; do
+for ckpt_path in "${ckpt_paths[@]}"; do
   for env_name in "${env_names[@]}"; do
     EvalSim
   done
@@ -50,7 +51,7 @@ declare -a scene_names=(
 )
 
 for scene_name in "${scene_names[@]}"; do
-  for policy_model in "${policy_models[@]}"; do
+  for ckpt_path in "${ckpt_paths[@]}"; do
     for env_name in "${env_names[@]}"; do
       EXTRA_ARGS="--additional-env-build-kwargs shader_dir=rt"
       EvalSim
@@ -62,7 +63,7 @@ done
 # lightings
 scene_name=frl_apartment_stage_simple
 
-for policy_model in "${policy_models[@]}"; do
+for ckpt_path in "${ckpt_paths[@]}"; do
   for env_name in "${env_names[@]}"; do
     EXTRA_ARGS="--additional-env-build-kwargs shader_dir=rt light_mode=brighter"
     EvalSim
@@ -75,7 +76,7 @@ done
 # new cabinets
 scene_name=frl_apartment_stage_simple
 
-for policy_model in "${policy_models[@]}"; do
+for ckpt_path in "${ckpt_paths[@]}"; do
   for env_name in "${env_names[@]}"; do
     EXTRA_ARGS="--additional-env-build-kwargs shader_dir=rt station_name=mk_station2"
     EvalSim