LeRobot — Gello + xArm6 Teleoperation

This is a fork of LeRobot with added support for Gello leader (Dynamixel XL330 servos) teleoperating a UFACTORY xArm6 follower.

Project Webpage

A static showcase for the 2D Vision Fetching course project lives in webpage/. It documents the full pipeline—Gello teleoperation, dual RealSense data collection, ACT training, and real xArm6 inference—with embedded demo videos.

File	Description
`webpage/index.html`	Project landing page (system overview, code pipeline, ACT details)
`webpage/robot_data_collection_1.mp4`	Teleoperation / data collection recording
`webpage/robot_data_collection_2.mp4`	Dual-view workspace recording
`webpage/robot_infer.mp4`	ACT policy rollout on the physical xArm6

Preview locally

Open webpage/index.html in a browser (keep the video files in the same directory). For a local HTTP preview:

cd webpage
python -m http.server 8080
# visit http://localhost:8080

Hardware Setup

Component	Hardware	Connection
Leader	Gello arm (7x Dynamixel XL330 servos)	USB-FTDI serial
Follower	UFACTORY xArm6 + gripper	Ethernet (default: 192.168.1.212)
Cameras	2x Intel RealSense D415/D435	USB 3.0

Installation

uv pip install -e . -i http://mirrors.aliyun.com/pypi/simple/

# Install xArm-Python-SDK manually (from UFACTORY source)
uv pip install xarm-python-sdk

# Install Intel RealSense SDK
uv pip install pyrealsense2

# For data collection
uv pip install 'lerobot[dataset]' -i http://mirrors.aliyun.com/pypi/simple/
uv pip install 'lerobot[dynamixel]'
uv pip install rerun-sdk

Calibration

Calibrate both devices before first use. Run each command separately:

# Calibrate the Gello leader arm
# Saved to ~/.cache/huggingface/lerobot/calibration/teleoperators/gello_leader/test.json
lerobot-calibrate \
  --teleop.type=gello_leader \
  --teleop.port=/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FTA2U1QU-if00-port0 \
  --teleop.id=test

# Calibrate the xArm6 follower
# ~/.cache/huggingface/lerobot/calibration/robots/xarm_follower/test.json
lerobot-calibrate \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=test

Gello raw (12-bit encoder + homing_offset)
  → Gello DEGREES normalization: (raw - range_mid) * 360 / 4095  → output degrees
    → Identity processor (1:1)
      → xArm revert: raw_xarm = degrees - xarm_homing_offset  → send to xArm

Follow the on-screen instructions to move each arm through its range of motion.

Teleoperate

Basic teleoperation (no cameras):

lerobot-teleoperate \
  --teleop.type=gello_leader \
  --teleop.port=/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FTA2U1QU-if00-port0 \
  --teleop.id=main \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=main

With cameras enabled:

lerobot-teleoperate \
  --teleop.type=gello_leader \
  --teleop.port=/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FTA2U1QU-if00-port0 \
  --teleop.id=main \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=main \
  --robot.cameras="{
    \"cam_arm\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"317622075882\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false},
    \"cam_front\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"231522072820\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false}
  }" \
  --fps=30

Record Data

Record a dataset by teleoperating the robot:

rm -rf out &&
export DISPLAY=:1 &&
lerobot-record \
  --teleop.type=gello_leader \
  --teleop.port=/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FTA2U1QU-if00-port0 \
  --teleop.id=main \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=main \
  --robot.cameras="{
    \"cam_arm\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"317622075882\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false, \"color_mode\": \"rgb\"},
    \"cam_front\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"231522072820\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false, \"color_mode\": \"rgb\"}
  }" \
  --dataset.repo_id=local/pick_and_place \
  --dataset.root=out \
  --dataset.single_task="put kettle on stove" \
  --dataset.num_episodes=3 \
  --dataset.episode_time_s=300 \
  --dataset.reset_time_s=5 \
  --dataset.fps=30 \
  --dataset.video=True \
  --dataset.streaming_encoding=true \
  --dataset.encoder_threads=16 \
  --dataset.push_to_hub=false

Recording Controls

Key	Action
Space / S	Start the current episode
Right Arrow	End current episode early / reset
Left Arrow	Re-record current episode
Esc	Stop recording entirely

Replay

Replay a recorded episode on the xArm6:

lerobot-replay \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=main \
  --dataset.repo_id=local/pick_and_place \
  --dataset.root=out \
  --dataset.episode=0

Viz dataset

export DISPLAY=:1 &&
lerobot-dataset-viz \
  --repo-id pick_and_place \
  --episode-index 0 \
  --root ./out

Change --dataset.episode to replay a different episode (e.g., num_episodes - 1 for the last one).

Training

The merged dataset is at ./data/out_merged (40 episodes, 14,320 frames, pick and place task).

--policy.n_obs_steps controls whether the model sees a single frame or a temporal history of frames. For local training, --dataset.repo_id is just a label — it doesn't affect data loading when --dataset.root is set.

Standard ACT (single frame, `n_obs_steps=1`)

lerobot-train \
  --policy.type=act \
  --policy.push_to_hub=false \
  --policy.n_obs_steps=1 \
  --policy.chunk_size=100 \
  --policy.image_resize_size="[256, 320]" \
  --dataset.repo_id=local/pick_place \
  --dataset.root=./data/0430 \
  --batch_size=64 \
  --steps=20000 \
  --num_workers=16 \
  --save_freq=200

Deployment

Policy deployment uses lerobot-rollout. The policy type (act) is auto-detected from the checkpoint — no need to specify --policy.type on the CLI.

How it works

Component	Detail
Inference	Synchronous (`--inference.type=sync`, default). Each control tick runs preprocessor → policy → postprocessor inline, then sends the action to the robot.
Temporal ensembling	Inference-time only — no retraining needed. Every step queries the policy for a new `chunk_size=100` action chunk, then fuses it with previous chunks via exponentially-weighted averaging (coeff = `0.01`, per the original ACT paper). This produces fully closed-loop control at ~9ms latency, well within the ~33ms budget at 30 FPS.
`n_action_steps=1`	Required by the temporal ensembler: `update()` pops 1 fused action per call, so the control loop must query every step. The model still predicts 100-step chunks every time — the ensemble fuses overlapping predictions for the same future timestep.
Action space	Absolute joint positions (7 DoF: j1–j6 degrees + gripper 0–100%).
Start position	Robot interpolates from its current pose to `--start_position` before the control loop begins. On shutdown, `--return_to_initial_position=true` (default) smoothly returns to this same pose. Values are in raw hardware space (as shown in xArm Studio) — the robot's calibration is applied internally to convert to the normalized joint space that the policy expects.
Multi-episode	`--num_rollouts=N` runs N episodes back-to-back. Between episodes the robot returns to `--start_position` and the policy is reset so every episode starts from a consistent state.

Rollout Controls

Key	Action
Space	Start the next episode (after homing to start position)
Right Arrow	End current episode early → return to start → wait for Space
Esc	Stop rollout entirely

Run

lerobot-train \
  --policy.type=act \
  --policy.push_to_hub=false \
  --policy.n_obs_steps=2 \
  --policy.chunk_size=100 \
  --policy.image_resize_size="[256, 320]" \
  --dataset.repo_id=local/pick_place \
  --dataset.root=./data/0430 \
  --batch_size=16 \
  --steps=20000 \
  --num_workers=16 \
  --save_freq=2000

Set --policy.n_obs_steps to the desired number of history frames (e.g. 2, 3, 5). Higher values use more GPU memory since image token count scales as n_obs_steps × H × W.

Deployment

Policy deployment uses lerobot-rollout. The policy type (act) is auto-detected from the checkpoint — no need to specify --policy.type on the CLI.

How it works

Component	Detail
Inference	Synchronous (`--inference.type=sync`, default). Each control tick runs preprocessor → policy → postprocessor inline, then sends the action to the robot.
Temporal ensembling	Inference-time only — no retraining needed. Every step queries the policy for a new `chunk_size=100` action chunk, then fuses it with previous chunks via exponentially-weighted averaging (coeff = `0.01`, per the original ACT paper). This produces fully closed-loop control at ~9ms latency, well within the ~33ms budget at 30 FPS.
`n_action_steps=1`	Required by the temporal ensembler: `update()` pops 1 fused action per call, so the control loop must query every step. The model still predicts 100-step chunks every time — the ensemble fuses overlapping predictions for the same future timestep.
Action space	Absolute joint positions (7 DoF: j1–j6 degrees + gripper 0–100%).
Start position	Robot interpolates from its current pose to `--start_position` before the control loop begins. On shutdown, `--return_to_initial_position=true` (default) smoothly returns to this same pose. Values are in raw hardware space (as shown in xArm Studio) — the robot's calibration is applied internally to convert to the normalized joint space that the policy expects.
Multi-episode	`--num_rollouts=N` runs N episodes back-to-back. Between episodes the robot returns to `--start_position` and the policy is reset so every episode starts from a consistent state.

Rollout Controls

Key	Action
Space	Start the next episode (after homing to start position)
Right Arrow	End current episode early → return to start → wait for Space
Esc	Stop rollout entirely

Run

uv run lerobot-rollout \
  --strategy.type=base \
  --policy.path=checkpoints/history/120000/pretrained_model \
  --policy.temporal_ensemble_coeff=0.01 \
  --policy.n_action_steps=1 \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=main \
  --robot.cameras="{
    \"cam_arm\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"317622075882\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false},
    \"cam_front\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"231522072820\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false}
  }" \
  --start_position='{"j1.pos": -1.4, "j2.pos": 15.4, "j3.pos": -84.1, "j4.pos": -2.1, "j5.pos": 75.7, "j6.pos": 19.0, "gripper.pos": 400.0}' \
  --start_position_duration=3.0 \
  --fps=30 \
  --duration=30 \
  --num_rollouts=3

Run with recording (sentry strategy)

Switch --strategy.type from base to sentry to record both camera streams alongside robot state and policy actions:

rm -rf output &&
export DISPLAY=:1 &&
uv run lerobot-rollout \
  --strategy.type=sentry \
  --strategy.upload_every_n_episodes=5 \
  --policy.path=checkpoints/history/120000/pretrained_model \
  --policy.temporal_ensemble_coeff=0.01 \
  --policy.n_action_steps=1 \
  --robot.type=xarm_follower \
  --robot.ip=192.168.2.202 \
  --robot.id=main \
  --robot.cameras="{
    \"cam_arm\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"317622075882\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false},
    \"cam_front\": {\"type\": \"intelrealsense\", \"serial_number_or_name\": \"231522072820\", \"fps\": 30, \"width\": 640, \"height\": 480, \"use_depth\": false}
  }" \
  --start_position='{"j1.pos": -1.4, "j2.pos": 15.4, "j3.pos": -84.1, "j4.pos": -2.1, "j5.pos": 75.7, "j6.pos": 19.0, "gripper.pos": 400.0}' \
  --start_position_duration=3.0 \
  --dataset.repo_id=local/rollout_history \
  --dataset.root=output \
  --dataset.single_task="put kettle on stove" \
  --dataset.fps=30 \
  --dataset.video=True \
  --dataset.streaming_encoding=true \
  --dataset.encoder_threads=8 \
  --dataset.push_to_hub=false \
  --fps=30 \
  --duration=30 \
  --num_rollouts=40

Recorded datasets can be visualized with lerobot-dataset-viz and replayed with lerobot-replay.

Key parameters

Parameter	Description
`--policy.path`	Path to `pretrained_model/` directory
`--policy.temporal_ensemble_coeff`	`0.01` = standard ACT temporal ensembling (override from checkpoint `null`)
`--policy.n_action_steps`	Must be `1` with temporal ensembling (override from checkpoint `100`)
`--strategy.type`	`base` = inference only; `sentry` = inference + recording; `dagger` = human-in-the-loop
`--robot.ip`	xArm6 controller IP
`--robot.cameras`	Camera config dict (type, serial, resolution)
`--start_position`	Raw hardware values (copy from xArm Studio). Dict `{"j1.pos": raw_angle, ...}` or JSON file path
`--start_position_duration`	Seconds for the interpolation (default 3.0)
`--start_position_in_raw`	`true` (default) = values are raw hardware readings; auto-converted via calibration
`--fps`	Control loop frequency (match training data: 30)
`--duration`	Seconds per episode (`0` = infinite, until Ctrl+C or Right Arrow)
`--num_rollouts`	Number of episodes to run sequentially (default 1). Robot returns to start_position between episodes
`--display_data`	`true` to enable Rerun visualization
`--dataset.repo_id`	Dataset label (sentry/dagger modes; local identifier when `--dataset.push_to_hub=false`)
`--dataset.root`	Output directory for recorded data (sentry/dagger modes)
`--dataset.video`	`true` = store camera frames as video, more efficient than per-frame images
`--dataset.streaming_encoding`	`true` = encode video in background threads, avoids disk I/O blocking the control loop
`--dataset.push_to_hub`	`true` to auto-upload to Hugging Face Hub after each episode/session

Upstream

Built on LeRobot — an open-source library for end-to-end robot learning. See the upstream documentation for policies, environments, and more.

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LeRobot — Gello + xArm6 Teleoperation

Project Webpage

Preview locally

Hardware Setup

Installation

Calibration

Teleoperate

Record Data

Recording Controls

Replay

Viz dataset

Training

Standard ACT (single frame, n_obs_steps=1)

Deployment

How it works

Rollout Controls

Run

Deployment

How it works

Rollout Controls

Run

Run with recording (sentry strategy)

Key parameters

Upstream

Standard ACT (single frame, `n_obs_steps=1`)