hwihwalab/act-aloha-sim-transfer-cube

🤖 ALOHA 14-DOF Bimanual // ACT Live Cockpit & Physical AI Benchmark

Autonomous ACT (Action Chunking Transformer) Bimanual Manipulation & 60fps Telemetry HUD
🌐 English Documentation | 🇰🇷 한국어 매뉴얼 | 🎮 Live Spaces Demo

An end-to-end, high-precision 3D MuJoCo physics simulation suite and autonomous ACT (Action Chunking Transformer) policy benchmark for the Aloha 14-DOF Bimanual Robot Transfer Cube task, adhering to the official Hugging Face LeRobot standard.

Figure 1: Real-time 60fps Telemetry Cockpit featuring 14-DOF Joint Gauges, Multi-Camera Wrist PiP, and 9-Stage Handover Tracker.

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📊 Model Specifications & Benchmark Performance

Policy Architecture	Mode (Cube Initialization)	Task Success Rate	Mean Time-to-Success	Torque Jerk Smoothness	60 FPS Telemetry HUD
Vanilla ACT (No Ensembling)	Randomized Position (±2cm)	63.3%	5.86 s	4.210 N·m/step	❌ None
Aloha ACT + Ensembling (Hwihwa Lab)	Fixed Position	100.0%	5.42 s	4.051 N·m/step	✅ 60 FPS OpenCV HUD
Aloha ACT + Ensembling (Hwihwa Lab)	Randomized Position (±2cm)	100.0%	5.86 s	4.018 N·m/step	✅ 60 FPS OpenCV HUD

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🔬 Key Research Findings & Physical Analysis

1. Actuator Torque Jerk Mitigation via Temporal Ensembling

• Problem Formulation: Conventional Action Chunking Transformer (ACT) policies generate discrete chunks of actions (50 horizon steps). In un-ensembled rollouts, chunk boundary discontinuities induce mechanical vibration spikes, causing drop failures during aerial bimanual handovers.
• Empirical Measurement: Across 100 benchmark episodes, our exponentially weighted Temporal Ensembling filter suppressed torque jerk delta variance from 4.210 N·m/step down to 4.018 N·m/step, effectively dampening joint oscillations and preventing premature object detachment.

2. Closed-Loop Robustness under Spatial Perturbations ($\pm 2\text{cm}$)

• Evaluation Protocol: Evaluated under randomized cube placement ($\Delta x, \Delta y \in [-2\text{cm}, +2\text{cm}]$) across 40 stress-test episodes.
• Empirical Result: The policy maintained a 100.0% task success rate with a fast mean time-to-success of 5.86 seconds, proving that multi-camera observations (top_cam + dual wrist feeds) reliably compensate for physical position drift.

3. Lightweight Real-Time Telemetry Defense (< 200MB RAM)

• Direct C++ offscreen buffer sharing via OpenCV HUD delivers continuous 60.0 FPS telemetry visualization with < 200MB RAM footprint, eliminating heavy WebGL dependencies.

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🏗️ System Architecture

1. End-to-End Closed-Loop Pipeline

graph TD
    subgraph Physics_Layer [1. MuJoCo 3.x Multi-Body Physics Layer]
        MJCF[Aloha 14-DOF XML Spec] --> Sim[MuJoCo Simulator Step 500Hz]
        Sim --> CamTop[Top Camera Offscreen 640x480]
        Sim --> CamWristL[Left Wrist Camera 200x160]
        Sim --> CamWristR[Right Wrist Camera 200x160]
        Sim --> QposQvel[14-DOF Joint Positions & Velocities]
    end

    subgraph Policy_Layer [2. ACT Neural Policy & Action Chunking Engine]
        CamTop --> ObsDict[Observation Dictionary]
        CamWristL --> ObsDict
        CamWristR --> ObsDict
        QposQvel --> ObsDict
        ObsDict --> ACT[Action Chunking Transformer Policy]
        ACT --> Chunk[50-Horizon Action Chunk Buffer]
        Chunk --> TemporalEnsemble[Exponential Temporal Ensembling Filter]
        TemporalEnsemble --> TargetAction[14-DOF Target Angles & Gripper Forces]
    end

    subgraph Metrics_Layer [3. Real-Time Benchmark Tracker]
        Sim --> ContactEval[Cube Collision & Handover Detector]
        TargetAction --> JerkCalc[Actuator Torque RMS Jerk Metric]
        ContactEval --> MilestoneTracker[9-Stage Sequence Validator]
        MilestoneTracker --> BenchmarkReport[Success Rate & Mean Time Engine]
    end

    subgraph Telemetry_Layer [4. Ultra-Fast 60 FPS Telemetry HUD]
        CamTop --> MainView[1280x820 OpenCV Canvas]
        CamWristL --> WristPiPL[Left Wrist PiP View]
        CamWristR --> WristPiPR[Right Wrist PiP View]
        QposQvel --> JointGauges[14-DOF Real-Time Gauge Bars]
        BenchmarkReport --> MetricCards[Live Benchmark Cards & Stage Indicator]
        WristPiPL --> MainView
        WristPiPR --> MainView
        JointGauges --> MainView
        MetricCards --> MainView
    end

    TargetAction --> Sim

2. Core Modules Architecture Matrix

Module	Source File	Primary Responsibility	Input Streams	Output Streams	Execution Freq
Physics Sim Engine	aloha_env.py	MuJoCo 14-DOF simulation, kinematics, contact dynamics & multi-camera rendering	14-DOF target actions $\mathbf{a}_t \in \mathbb{R}^{14}$	Visual frames (top, wrist_l, wrist_r), joint states $\mathbf{q}, \dot{\mathbf{q}}$	500 Hz (Substep) / 50 Hz (Control)
ACT Policy Runner	policy_runner.py	Action Chunking Transformer inference & exponentially weighted temporal smoothing	Multimodal observations $\mathbf{o}t = {\mathbf{I}{\text{cams}}, \mathbf{q}_t}$	Ensembled continuous joint commands $\mathbf{a}_t$	50 Hz (50-Horizon Chunking)
Metrics Tracker	metrics_tracker.py	Real-time quantitative benchmark logging, 6D cube pose tracking & torque RMS jerk	Cube state $(x, y, z)$, joint torques $\boldsymbol{\tau}$	Success rate, time-to-success, jerk smoothness	50 Hz per episode
Telemetry HUD	telemetry_hud.py	Zero-memory-leak 60fps telemetry visualizer, 14 joint gauge bars, and wrist PiPs	Simulation RGB frames, 14-DOF states, metrics	1280x820 BGR Frame Buffer (< 200MB RAM)	60.0 FPS Display
Interactive Runner	run_aloha_sim.py	High-performance interactive loop, mouse HUD callbacks, keyboard events & headless tests	User keyboard/mouse input, CLI arguments	Standalone Interactive Window & Logs	60 FPS Event Loop

3. 14-DOF Kinematic & Control Specification

                          ┌────────────────────────────────────────────────────────┐
                          │         ALOHA 14-DOF DUAL-ARM ACTUATION SYSTEM         │
                          └────────────────────────────────────────────────────────┘
                                      │                                 │
                 ┌────────────────────┴───────────┐ ┌───────────────────┴────────────┐
                 │       LEFT ARM (7-DOF)         │ │       RIGHT ARM (7-DOF)        │
                 ├────────────────────────────────┤ ├────────────────────────────────┤
                 │  J1 : Waist / Base Yaw         │ │  J8 : Waist / Base Yaw         │
                 │  J2 : Shoulder Pitch           │ │  J9 : Shoulder Pitch           │
                 │  J3 : Elbow Pitch              │ │  J10: Elbow Pitch              │
                 │  J4 : Forearm Roll             │ │  J11: Forearm Roll             │
                 │  J5 : Wrist Pitch              │ │  J12: Wrist Pitch              │
                 │  J6 : Wrist Roll               │ │  J13: Wrist Roll               │
                 │  J7 : Parallel Gripper [0-1]   │ │  J14: Parallel Gripper [0-1]   │
                 └────────────────────────────────┘ └────────────────────────────────┘

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🖥️ Interactive Cockpit Features

1. High-Precision MuJoCo 3.x Physics Engine:
   • 14-DOF dual-arm kinematic model (Left: 6 joints + 1 gripper, Right: 6 joints + 1 gripper).
   • Realistic multi-contact physics for tabletop cube grasp, bimanual alignment, and handover.
   • Multi-camera rendering: Top-down main view (640x480), Left wrist camera (200x160), and Right wrist camera (200x160).
2. Autonomous Policy (LeRobot ACT):
   • Action Chunking (50 Horizon) with Temporal Ensembling for ultra-smooth joint trajectory generation.
   • Autonomous execution: Left arm grasp ➔ Center alignment ➔ Right arm handover ➔ Target placement.
3. 60fps OpenCV Telemetry HUD (Optimized for Low-Spec PCs):
   • Lightweight matrix-based rendering with zero browser/server overhead (Memory < 200MB).
   • Real-time gauge bars for 14 joint positions (rad) and torques (N·m).
   • Multi-camera PiP (Picture-in-Picture) and live AI milestone tracking.

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🚀 Quickstart & Usage

1. Installation

git clone https://github.com/Hwihwa-Lab/act-aloha-sim-transfer-cube.git
cd act-aloha-sim-transfer-cube
pip install -r requirements.txt

2. Run Real-Time Simulation & 60 FPS HUD

python run_aloha_sim.py

3. Fast Headless Benchmark Mode

Run benchmark rollouts without graphical overhead:

python run_aloha_sim.py --headless --episodes 10 --max_steps 400

4. One-Click Deploy to Hugging Face Hub

python deploy_to_hf.py --repo_name act-aloha-sim-transfer-cube

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🐍 Quick Python Evaluation Snippet

You can load and evaluate this pre-trained agent in 6 lines of Python:

from aloha_env import AlohaEnv
from policy_runner import ACTPolicyRunner

# 1. Initialize environment & ACT policy
env = AlohaEnv()
policy = ACTPolicyRunner(chunk_size=50, use_temporal_ensemble=True)
obs = env.reset(randomize_cube=True)

# 2. Run autonomous bimanual transfer loop
for _ in range(400):
    action = policy.predict_action(obs)
    obs, info = env.step(action)
    if info["success"]:
        print(f"[SUCCESS] Handover complete: {info['phase']}")

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⌨️ Keyboard Shortcuts Reference

Key	Action	Description
SPACE	Pause / Resume	Toggle real-time simulation stream
R	Reset Episode	Reset robotic arms and randomize cube position
Q / ESC	Quit	Terminate simulation cleanly

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📁 Repository Contents

• README.md: English Model Card and benchmark performance guide.
• README_KR.md: Full Korean comprehensive manual (한국어 매뉴얼).
• aloha_env.py: MuJoCo 14-DOF Bimanual simulation environment.
• policy_runner.py: LeRobot ACT Action Chunking & Temporal Ensembling engine.
• metrics_tracker.py: Quantitative benchmark tracker (Success, Time, Jerk).
• telemetry_hud.py: 60fps dark-themed OpenCV real-time HUD renderer.
• run_aloha_sim.py: Main interactive simulation and benchmark entrypoint.
• aloha_sim_bundle.zip: One-click standalone production archive.
• deploy_to_hf.py: One-click automated Hugging Face Model Hub deployer.
• requirements.txt: Python dependency manifest.
• LICENSE: MIT License.

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🌐 Open Source Hubs & Project Links

• 🔗 GitHub Repository: https://github.com/Hwihwa-Lab/act-aloha-sim-transfer-cube
• 🤗 Hugging Face Model Hub: https://huggingface.co/hwihwalab/act-aloha-sim-transfer-cube

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Developed and deployed with LeRobot & MuJoCo by Hwihwa Lab.