README.md

metadata

license: mit
task_categories:
  - robotics
tags:
  - lerobot
  - so101
  - act
  - reinforcement-learning
  - exploration
  - outcome-labeled
  - manipulation
size_categories:
  - n<1K

so101_pick_diverse_objects_rl_round1

Dataset Description

This dataset contains exploration rollout episodes with outcome labels collected on the SO-101 robot arm pair using rl_rollout_act.py.

The collection procedure is as follows:

1. An ACT policy (trained on HIL-corrected data; see so101_pick_diverse_objects_hil_round1) drives the follower arm autonomously.
2. Gaussian noise is injected into the ACT latent space (z) at each step to encourage exploration beyond the nominal policy distribution.
3. At the end of each episode, the human operator labels the outcome as success or failure.
4. Every saved episode includes next.reward and next.done columns: 0 / 0 for all frames, with the final frame of a success episode set to reward = 1, done = 1.

This format makes the dataset directly compatible with REINFORCE-style policy updates (filtering to success episodes only, via train_act_filtered.py) and binary reward classifier training (via train_reward_classifier.py).

Task

Pick diverse objects — the robot arm picks up a variety of objects placed on a tabletop. Multiple task prompts are supported (e.g., "Pick the red cube.", "Pick a pen."); the task string is assigned at the start of each episode.

Key Differences from the HIL Dataset

Property	hil_round1	rl_round1 (this dataset)
Data source	Human intervention corrections	Autonomous rollouts with noise
Human role	Takes over during failures	Labels outcome at episode end
Exploration mechanism	Human guidance	Gaussian latent-space noise (--noise_std)
Reward signal	Not included	next.reward, next.done per frame
Primary use	Policy fine-tuning (BC)	REINFORCE update / reward classifier

Related Resources

Resource	Link
Seed HIL dataset (Round 1)	TakuyaHiraoka/so101_pick_diverse_objects_hil_round1
Policy trained on HIL data	TakuyaHiraoka/act_so101_pick_diverse_objects
Full pipeline & scripts	TakuyaHiraoka/LeRobot-Playground

Hardware

• Robot: SO-101 leader–follower arm pair
• Camera: USB or IP/MJPEG stream (640 px wide)
• Framework: LeRobot == 0.4.4

Data Collection Procedure

The dataset was collected using rl_rollout_act.py from LeRobot-Playground.

Key keyboard controls during recording:

Key	Action
s	Save current episode as success
f	Save current episode as failure
r	Discard and re-record
q	End the session

On timeout, the terminal always prompts for an outcome label — every saved episode is guaranteed to have a success or failure annotation.

Example launch command:

python rl_rollout_act.py \
    --follower_port /dev/ttyACM1 --follower_id <follower-id> \
    --leader_port   /dev/ttyACM0 --leader_id   <leader-id> \
    --camera_url    "http://<camera-host>:8080" \
    --policy_path   <path-to-pretrained-act> \
    --repo_id       TakuyaHiraoka/so101_pick_diverse_objects_rl_round1 \
    --tasks         "Pick a pen." \
    --num_episodes 20 --episode_time_s 20 --reset_time_s 5 --fps 30 \
    --noise_std 0.05 --resume

Downstream Usage

REINFORCE-style ACT re-training (success filtering)

python train_act_filtered.py \
    --dataset_repo_id TakuyaHiraoka/so101_pick_diverse_objects_rl_round1 \
    --base_policy     <path-to-act-v1> \
    --output_dir      outputs/train/act_v2 \
    -- \
    --steps 30000 --batch_size 64 --policy.optimizer_lr 1e-5

train_act_filtered.py scans for episodes where max(next.reward) > threshold and passes only those to lerobot-train. For binary reward with no advantage, this is equivalent to a REINFORCE update:

∇θ J = E_τ [ ∇θ log π(τ) · R(τ) ]

Binary reward classifier training

python train_reward_classifier.py \
    --dataset_repo_id TakuyaHiraoka/so101_pick_diverse_objects_rl_round1 \
    --output_dir      outputs/reward_clf/round1 \
    --epochs 20 --batch_size 64

Trains a ResNet-18 head to predict P(success | s) per frame. Validation is held out at the episode level to prevent data leakage.

Intended Use

This dataset is intended for:

• REINFORCE / filtered BC — re-train the ACT policy on success episodes only.
• Learned value / reward functions — train a binary success classifier as an approximate value function.

License

MIT