QROOT: An Integrated Diffusion Transformer and Reinforcement Learning Approach for Quadrupedal Locomotion

QROOT: An Integrated Diffusion Transformer and Reinforcement Learning Approach for Quadrupedal Locomotion#

QROOT

📝 Abstract#

Quadrupedal robots offer superior mobility in unstructured environments, yet lack the generalist autonomy seen in recent humanoid systems. In this work, we present QROOT, a novel adaptation of the GR00T N1 foundation model to quadrupedal platforms. QROOT enables a quadrupedal robot to interpret natural language instructions, perceive its environment, and generate locomotion-centric behaviors through a unified Vision-Language-Action (VLA) framework.

To bridge the embodiment gap, we introduce a control stack that combines diffusion transformer with a reinforcement learning-based stabilizer(PPO), enabling smooth and robust execution on real-world hardware. Our approach generalizes effectively to navigation, search, and object-localization tasks with minimal fine-tuning, demonstrating the feasibility of transferring generalist robot models to mobile, legged platforms.

🎯 Key Features#

  • Natural Language Understanding: Interpret and execute natural language commands

  • Environment Perception: Advanced vision-based environment understanding

  • Robust Locomotion: Combined diffusion transformer and PPO-based control

  • Task Generalization: Effective performance across multiple task types

  • Real-world Deployment: Hardware-ready implementation

🔬 Methodology#

Algorithm Architecture#

QROOT Architecture

Simulation Environments#

We conducted simulation using IsaacSim, designing three distinct environments for different task types:

env

Task Categories#

  • 🎯 Object-localization tasks

  • 🔍 Search Tasks

  • 🗺️ Navigation Tasks

📊 Dataset Creation#

We developed a user-friendly interface for robot teleoperation and dataset management, enabling:

  • Direct movement control

  • Real-time simulation

  • Systematic data collection

Control Interface#

  • W: Forward movement

  • S: Backward movement

  • D: Right movement

  • A: Left movement

Demonstration of keyboard-based robot control in IsaacSim

🎓 Training Configuration#

Parameter

Value

Hardware

NVIDIA A6000 GPU

Batch Size

16

Training Steps

1,000

Optimizer

AdamW

Learning Rate

1×10⁻⁴

β₁

0.95

β₂

0.999

Epsilon

1×10⁻⁸

Weight Decay

1×10⁻⁵

Learning Rate Schedule

Cosine Annealing

Warm-up Ratio

0.05

🎮 Policy Execution#

Object Localization Tasks#

🟦 Go to Blue Cube#

🟥 Go to Red Cube#

Search Tasks#

🪑 Find Chair#

🛋️ Find Sofa#

📝 Conclusion#

In this study, we adapt the GR00T N1 architecture to quadrupedal robots through a hybrid framework that combines a Vision-Language-Action (VLA) model with Proximal Policy Optimization (PPO). This integration enables robust, adaptable locomotion by uniting high-level semantic understanding with stable, reinforcement learning-based control.

Our results highlight the potential of combining model-based and learning-based approaches for generalist robotics. Future work will explore:

  • More complex task scenarios

  • Enhanced sensory input integration

  • Cross-platform policy transfer

  • Improved real-world versatility

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