Evo-RL

SJTU & Evo-Tech

Architecture Overview

🎯 Evo-RL Focus

• Open real-world RL on two platforms: we build and release full real-world RL pipelines on SO101 and AgileX (PiPER/PiPER-X).
• Open code, models, and datasets for reproducibility: we continuously release runnable offline RL assets so more people can reproduce results and apply them to real-world tasks.
• Open algorithm and community co-evolution: we reproduce existing real-world RL methods, propose new methods, and keep publishing data/benchmarks to grow a collaborative open-source community.

🚀 News

• [2026-06-30] Added a detailed Hugging Face dataset card for the RW-RL Dataset, including visual examples, release statistics, file layout, modalities, and download instructions.
• [2026-03-07] Added AgileX (PiPER/PiPER-X) support for real-world RL.
• [2026-02-26] First SO101 real-world RL baseline and reproducible CLI workflow are released.

🧭 Table of Contents

Value Visual Results

Success Case

Failure Case

Policy Rollout Visual Results

Human-in-the-Loop Visual Results

⚡ Quick Start

LeRobot-aligned foundation: we use LeRobot as the base of this codebase because its inference and data-collection logic are highly aligned with real-world RL workflows.

1) Installation

git clone https://github.com/MINT-SJTU/Evo-RL.git
cd Evo-RL
conda create -y -n evo-rl python=3.10
conda activate evo-rl
pip install -e .

For setup details and platform-specific dependencies, follow the official LeRobot configuration guide.

2) Hardware Setup

SO Series (SO100/SO101)

For SO-series setup, please follow the official tutorial in detail and complete all installation and configuration steps there before continuing. The examples below use SO101 as the reference configuration.

Device path recommendation

Recommended path strategy:

• Robot serial: use /dev/serial/by-id/ (stable across reboots).
• Cameras: prefer /dev/v4l/by-id/; if IDs are not unique, use /dev/v4l/by-path/.
• In examples below: robot ports use by-id, camera paths use by-path.

You can inspect available stable paths with:

ls -l /dev/serial/by-id/
ls -l /dev/v4l/by-id/
ls -l /dev/v4l/by-path/

For single-arm users, no major changes are required. After setup, run the command below to verify your system is ready for the next stage:

lerobot-teleoperate \
  --robot.type=so101_follower \
  --robot.port=/dev/serial/by-id/<SO101_FOLLOWER_PORT> \
  --robot.id=my_so101_follower \
  --teleop.type=so101_leader \
  --teleop.port=/dev/serial/by-id/<SO101_LEADER_PORT> \
  --teleop.id=my_so101_leader

For dual-arm users, we recommend mirroring the mechanical parts corresponding to servos 4/5/6 on the left leader and left follower arms, which usually provides a more natural bimanual operation feel.

Before running the dual-arm command, make sure calibration files exist under ~/.cache/huggingface/lerobot/calibration/ like:

calibration/
├── robots
│   └── so_follower
│       ├── bi_so101_follower_left.json
│       └── bi_so101_follower_right.json
└── teleoperators
    └── so_leader
        ├── bi_so101_leader_left.json
        └── bi_so101_leader_right.json

This layout is slightly different from single-arm setup.

Then run this command to verify dual-arm setup:

lerobot-teleoperate \
  --robot.type=bi_so_follower \
  --robot.left_arm_config.port=/dev/serial/by-id/<LEFT_FOLLOWER_PORT> \
  --robot.right_arm_config.port=/dev/serial/by-id/<RIGHT_FOLLOWER_PORT> \
  --robot.id=bi_so101_follower \
  --teleop.type=bi_so_leader \
  --teleop.left_arm_config.port=/dev/serial/by-id/<LEFT_LEADER_PORT> \
  --teleop.right_arm_config.port=/dev/serial/by-id/<RIGHT_LEADER_PORT> \
  --teleop.id=bi_so101_leader

Camera configuration

Before data collection, validate camera mapping first.

Check whether each camera supports your target setting (for example, 640x480 @ 30):

v4l2-ctl -d /dev/v4l/by-path/<CAM_PATH> --list-formats-ext

Single-arm camera check (example):

lerobot-teleoperate \
  --robot.type=so101_follower \
  --robot.port=/dev/serial/by-id/<SO101_FOLLOWER_PORT> \
  --robot.id=my_so101_follower \
  --robot.cameras='{ front: {type: opencv, index_or_path: "/dev/v4l/by-path/<FRONT_CAM>", width: 640, height: 480, fps: 30}}' \
  --teleop.type=so101_leader \
  --teleop.port=/dev/serial/by-id/<SO101_LEADER_PORT> \
  --teleop.id=my_so101_leader \
  --display_data=true

Dual-arm camera check (example):

lerobot-teleoperate \
  --robot.type=bi_so_follower \
  --robot.left_arm_config.port=/dev/serial/by-id/<LEFT_FOLLOWER_PORT> \
  --robot.right_arm_config.port=/dev/serial/by-id/<RIGHT_FOLLOWER_PORT> \
  --robot.id=my_bi_so101_follower \
  --robot.left_arm_config.cameras='{ wrist: {type: opencv, index_or_path: "/dev/v4l/by-path/<LEFT_WRIST_CAM_PATH>", width: 640, height: 480, fps: 30}}' \
  --robot.right_arm_config.cameras='{ wrist: {type: opencv, index_or_path: "/dev/v4l/by-path/<RIGHT_WRIST_CAM_PATH>", width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: "/dev/v4l/by-path/<FRONT_CAM_PATH>", width: 640, height: 480, fps: 30}}' \
  --teleop.type=bi_so_leader \
  --teleop.left_arm_config.port=/dev/serial/by-id/<LEFT_LEADER_PORT> \
  --teleop.right_arm_config.port=/dev/serial/by-id/<RIGHT_LEADER_PORT> \
  --teleop.id=my_bi_so101_leader \
  --display_data=true

For dual-arm camera mapping, it is fine to attach front under either the left-arm or right-arm camera config. If you use more camera views, place them under either the left or right arm camera config as well.

If needed, you can also use temporary device paths (for example /dev/ttyACM* and /dev/video*) during initial debugging.

AgileX (PiPER/PiPER-X)

PiPER commands use the USB-CAN adapter's stable ID_SERIAL_SHORT instead of a temporary Linux canN name. List the connected adapters with:

for device in /sys/class/net/*; do
  [ "$(cat "$device/type" 2>/dev/null)" = "280" ] || continue
  serial=$(udevadm info --query=property --path="$device" | sed -n 's/^ID_SERIAL_SHORT=//p')
  echo "$(basename "$device"): $serial"
done

Then configure the adapters needed for the run:

lerobot-setup-can --mode=setup \
  --usb_can_serials=<USB_CAN_SERIAL_1>,<USB_CAN_SERIAL_2>,<USB_CAN_SERIAL_3>,<USB_CAN_SERIAL_4>

For single-arm users, run the command below to verify the system is ready:

lerobot-teleoperate \
  --robot.type=piperx_follower \
  --robot.port=<FOLLOWER_USB_CAN_SERIAL> \
  --robot.id=my_piperx_follower \
  --teleop.type=piperx_leader \
  --teleop.port=<LEADER_USB_CAN_SERIAL> \
  --teleop.id=my_piperx_leader

For bimanual users, run this command to verify dual-arm teleoperation:

lerobot-teleoperate \
  --robot.type=bi_piperx_follower \
  --robot.id=my_bi_piperx_follower \
  --robot.left_arm_config.port=<LEFT_FOLLOWER_USB_CAN_SERIAL> \
  --robot.right_arm_config.port=<RIGHT_FOLLOWER_USB_CAN_SERIAL> \
  --teleop.type=bi_piperx_leader \
  --teleop.id=my_bi_piperx_leader \
  --teleop.left_arm_config.port=<LEFT_LEADER_USB_CAN_SERIAL> \
  --teleop.right_arm_config.port=<RIGHT_LEADER_USB_CAN_SERIAL>

For PiPER (non-X), replace bi_piperx_follower/bi_piperx_leader with bi_piper_follower/bi_piper_leader.

3) Data Collection

Collect rollout data with lerobot-human-inloop-record.

SO Series (SO100/SO101)

Bimanual template:

lerobot-human-inloop-record \
  --robot.type=bi_so_follower \
  --robot.left_arm_config.port=/dev/serial/by-id/<LEFT_FOLLOWER_PORT> \
  --robot.right_arm_config.port=/dev/serial/by-id/<RIGHT_FOLLOWER_PORT> \
  --robot.id=my_bi_so101_follower \
  --robot.left_arm_config.cameras='{ wrist: {type: opencv, index_or_path: "/dev/v4l/by-path/<LEFT_WRIST_CAM_PATH>", width: 640, height: 480, fps: 30, fourcc: "MJPG"}}' \
  --robot.right_arm_config.cameras='{ wrist: {type: opencv, index_or_path: "/dev/v4l/by-path/<RIGHT_WRIST_CAM_PATH>", width: 640, height: 480, fps: 30, fourcc: "MJPG"}, front: {type: intelrealsense, serial_number_or_name: "<REALSENSE_SN>", width: 640, height: 480, fps: 30, warmup_s: 2}}' \
  --teleop.type=bi_so_leader \
  --teleop.left_arm_config.port=/dev/serial/by-id/<LEFT_LEADER_PORT> \
  --teleop.right_arm_config.port=/dev/serial/by-id/<RIGHT_LEADER_PORT> \
  --teleop.id=my_bi_so101_leader \
  --dataset.repo_id=<HF_USERNAME_OR_ORG>/<DATASET_NAME> \
  --dataset.single_task="<YOUR_TASK_DESCRIPTION>" \
  --dataset.num_episodes=<NUM_EPISODES> \
  --dataset.episode_time_s=<EPISODE_SECONDS> \
  --dataset.reset_time_s=<RESET_SECONDS> \
  --dataset.push_to_hub=true \
  --display_data=true

Recommendation: use fourcc: "MJPG" for OpenCV and warmup_s for RealSense. In this example front uses RealSense, but you can switch it to OpenCV with the same structure.

AgileX (PiPER/PiPER-X)

Bimanual template (left/right, PiPER-X example):

lerobot-human-inloop-record \
  --robot.type=bi_piperx_follower \
  --robot.id=my_bi_piperx_follower \
  --robot.left_arm_config.port=<LEFT_FOLLOWER_USB_CAN_SERIAL> \
  --robot.right_arm_config.port=<RIGHT_FOLLOWER_USB_CAN_SERIAL> \
  --teleop.type=bi_piperx_leader \
  --teleop.id=my_bi_piperx_leader \
  --teleop.left_arm_config.port=<LEFT_LEADER_USB_CAN_SERIAL> \
  --teleop.right_arm_config.port=<RIGHT_LEADER_USB_CAN_SERIAL> \
  --dataset.repo_id=<HF_USERNAME_OR_ORG>/<DATASET_NAME> \
  --dataset.single_task="<YOUR_TASK_DESCRIPTION>" \
  --dataset.num_episodes=<NUM_EPISODES> \
  --dataset.episode_time_s=<EPISODE_SECONDS> \
  --dataset.reset_time_s=<RESET_SECONDS> \
  --dataset.push_to_hub=true \
  --display_data=true

Hotkeys:

• i: toggle intervention mode (policy <-> teleop takeover)
• s: mark success and end current episode
• f: mark failure and end current episode
• Right Arrow: end the current loop early
• Left Arrow: end early and re-record the current episode
• Esc: stop the recording session

Quick quality check:

lerobot-dataset-report --dataset <HF_USERNAME_OR_ORG>/<DATASET_NAME>

This prints: dataset meta, totals, episode-length stats/histogram, success/intervention metrics, task list, and full feature schema.

4) Value Function Training

Train the value function on the current dataset. Current default: Pi*0.6 (--value.type=pistar06).

Single-GPU template:

lerobot-value-train \
  --dataset.repo_id=<HF_USERNAME_OR_ORG>/<DATASET_NAME> \
  --value.type=pistar06 \
  --value.dtype=bfloat16 \
  --value.push_to_hub=true \
  --value.repo_id=<HF_USERNAME_OR_ORG>/<VALUE_MODEL_REPO> \
  --batch_size=64 \
  --output_dir=outputs/value_train/<RUN_NAME> \
  --job_name=<RUN_NAME> \
  --wandb.enable=true

Multi-GPU template:

CUDA_VISIBLE_DEVICES=<GPU_ID_LIST> accelerate launch \
  --multi_gpu \
  --num_processes=<NUM_GPUS> \
  --mixed_precision=bf16 \
  $(which lerobot-value-train) \
  --batch_size=32/<NUM_GPUS> \
  <VALUE_TRAIN_ARGS>

To plug in a different value function, minimal path in this repo:

• Add src/lerobot/values/<your_value>/configuration_<your_value>.py with @PreTrainedConfig.register_subclass("<your_value>").
• Add src/lerobot/values/<your_value>/modeling_<your_value>.py with <YourValue>Policy(PreTrainedPolicy) (implement at least forward, predict_value, and build_training_raw_batch_hook for lerobot-value-train).
• Add src/lerobot/values/<your_value>/processor_<your_value>.py with make_<your_value>_pre_post_processors(...).
• Remove/replace the current pistar06-only type checks in src/lerobot/configs/value_train.py and src/lerobot/scripts/lerobot_value_infer.py.

5) Value Inference

Infer value signals and write value/advantage/indicator back to the dataset:

• value: estimated return-to-go of the current frame.
• advantage: relative improvement signal (higher means better-than-baseline trajectory quality).
• indicator: binarized training tag derived from advantage.

Single-GPU template:

lerobot-value-infer \
  --dataset.repo_id=<HF_USERNAME_OR_ORG>/<DATASET_NAME> \
  --inference.checkpoint_path=outputs/value_train/<RUN_NAME> \
  --runtime.device=cuda \
  --runtime.batch_size=64 \
  --acp.enable=true \
  --acp.n_step=50 \
  --acp.positive_ratio=0.3 \
  --acp.value_field=complementary_info.value_<TAG> \
  --acp.advantage_field=complementary_info.advantage_<TAG> \
  --acp.indicator_field=complementary_info.acp_indicator_<TAG> \
  --output_dir=outputs/value_infer/<RUN_NAME> \
  --job_name=<RUN_NAME>.infer

Multi-GPU template:

CUDA_VISIBLE_DEVICES=<GPU_ID_LIST> accelerate launch \
  --multi_gpu \
  --num_processes=<NUM_GPUS> \
  --mixed_precision=bf16 \
  $(which lerobot-value-infer) \
  <VALUE_INFER_ARGS>

Parameter notes:

--acp.n_step: n-step advantage horizon.
--acp.positive_ratio: positive label ratio after advantage binarization (e.g., 0.3 = top 30% per task).

Expected new columns:

complementary_info.value_<TAG>
complementary_info.advantage_<TAG>
complementary_info.acp_indicator_<TAG>

These columns are written back to the original dataset specified by --dataset.repo_id.

6) Policy Training

Train the policy with advantage-conditioned tags. Policy requirement: it must support text/task input, because Advantage-Conditioned tags are injected into task text.

Single-GPU template:

lerobot-train \
  --dataset.repo_id=<HF_USERNAME_OR_ORG>/<DATASET_NAME> \
  --policy.type=<POLICY_TYPE> \
  --policy.pretrained_path=<POLICY_PRETRAINED_PATH> \
  --policy.device=cuda \
  --policy.dtype=bfloat16 \
  --batch_size=32 \
  --steps=30000 \
  --acp.enable=true \
  --acp.indicator_field=complementary_info.acp_indicator_<TAG> \
  --acp.indicator_dropout_prob=0.3 \
  --output_dir=outputs/train/<RUN_NAME> \
  --job_name=<RUN_NAME> \
  --wandb.enable=true \
  --policy.push_to_hub=true \
  --policy.repo_id=<HF_USERNAME_OR_ORG>/<POLICY_REPO>

--acp.indicator_dropout_prob controls tag drop rate in task text; 0.3 helps learn both tagged and untagged conditions.

Important checks:

• --acp.indicator_field must exist in the dataset and be binary (0/1).

Multi-GPU template:

CUDA_VISIBLE_DEVICES=<GPU_ID_LIST> accelerate launch \
  --multi_gpu \
  --num_processes=<NUM_GPUS> \
  --mixed_precision=bf16 \
  $(which lerobot-train) \
  --batch_size=32/<NUM_GPUS> \
  <POLICY_TRAIN_ARGS>

7) Closed-loop Rollout and Next Round

Deploy the trained policy in human-in-loop mode and collect the next dataset round:

lerobot-human-inloop-record \
  --robot.type=bi_so_follower \
  --robot.left_arm_config.port=/dev/serial/by-id/<LEFT_FOLLOWER_PORT> \
  --robot.right_arm_config.port=/dev/serial/by-id/<RIGHT_FOLLOWER_PORT> \
  --robot.id=my_bi_so101_follower \
  --robot.left_arm_config.cameras='{ wrist: {type: opencv, index_or_path: "/dev/v4l/by-path/<LEFT_WRIST_CAM_PATH>", width: 640, height: 480, fps: 30, fourcc: "MJPG"}}' \
  --robot.right_arm_config.cameras='{ wrist: {type: opencv, index_or_path: "/dev/v4l/by-path/<RIGHT_WRIST_CAM_PATH>", width: 640, height: 480, fps: 30, fourcc: "MJPG"}, front: {type: intelrealsense, serial_number_or_name: "<REALSENSE_SN>", width: 640, height: 480, fps: 30, warmup_s: 2}}' \
  --teleop.type=bi_so_leader \
  --teleop.left_arm_config.port=/dev/serial/by-id/<LEFT_LEADER_PORT> \
  --teleop.right_arm_config.port=/dev/serial/by-id/<RIGHT_LEADER_PORT> \
  --teleop.id=my_bi_so101_leader \
  --dataset.repo_id=<HF_USERNAME_OR_ORG>/<DATASET_NAME_NEXT_ROUND> \
  --dataset.single_task="<YOUR_TASK_DESCRIPTION>" \
  --dataset.num_episodes=<NUM_EPISODES> \
  --dataset.episode_time_s=<EPISODE_SECONDS> \
  --dataset.reset_time_s=<RESET_SECONDS> \
  --dataset.push_to_hub=true \
  --display_data=true \
  --policy.path=<POLICY_CHECKPOINT_OR_HUB_ID> \
  --resume=true

Dataset continuation options:

• Append in place: keep --resume=true and continue recording into the same dataset.
• Merge multiple rounds: use the official dataset editor to merge separate datasets.

lerobot-edit-dataset \
  --repo_id=<HF_USERNAME_OR_ORG>/<MERGED_DATASET_NAME> \
  --operation.type=merge \
  --operation.repo_ids="['<HF_USERNAME_OR_ORG>/<DATASET_ROUND_1>','<HF_USERNAME_OR_ORG>/<DATASET_ROUND_2>']"

Additional data attributes vs default lerobot-record behavior:

• complementary_info.policy_action: policy output action at each step.
• complementary_info.is_intervention: whether current step is in intervention.
• complementary_info.state: intervention state-machine state.
• complementary_info.collector_policy_id: step-level action source ID (human or policy ID).
• Episode metadata episode_success: success/failure label saved per episode.

Iterative training loop (abstract):

[Multi-task demonstration data pool]
        |
        v
[Offline RL pretraining for a vision-language-action policy]
        |
        v
[Task-specific initialization / fine-tuning from demonstrations]
        |
        v
|---- Iteration k = 1..K -------------------------------------|
| 1) Deploy current policy π_k and collect new rollout data   |
| 2) Merge into data pool: D <- D U new_data                  |
| 3) Train value function on D                                |
| 4) Infer advantage and binarize into indicator tags         |
| 5) Train advantage-conditioned policy to get π_{k+1}        |
|-------------------------------------------------------------|
        |
        v
[Stronger policy with improved success rate and throughput]

Model & Dataset

• Hugging Face model release: coming soon
• Hugging Face dataset: MINT-SJTU/RW-RL-Dataset

RW-RL Dataset is the companion real-world reinforcement learning dataset for Evo-RL. It is organized around iterative policy improvement on real robots, including teleoperation demonstrations, human-in-the-loop intervention data, policy rollout traces, episode-level success/failure labels, intervention states, and complementary signals used for value/reward modeling.

The dataset is designed to support offline RL, value learning, advantage-conditioned policy training, and closed-loop rollout analysis across real robot tasks. Please refer to the Hugging Face dataset card for release notes, schema details, splits, and version tags.

Community Channels

• WeChat official post: Coming Soon
• Documentation: docs/README.md
• GitHub Issues: Create an issue
• Email: business@evomind-tech.com
• Get into WeChat group by scanning QR code:

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• Get into WeChat Group or get So101 Supplier, please contact the following WeChat, 加群（注明加真机强化学习群）/So101 设备提供商:

Affiliations

Citation

@misc{evorl2026,
  title        = {Evo-RL: Towards Iterative Policy Improvement in Real-World Offline RL},
  author       = {Evo-RL Contributors},
  year         = {2026},
  howpublished = {\url{https://github.com/MINT-SJTU/Evo-RL}}
}

License

Apache-2.0. See LICENSE.

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