AgiBot D1 EDU Quadruped Robot Dog (Development Edition)

• AgiBot D1 EDU Quadruped Robot Dog (Development Edition)
• 12-DoF aluminum alloy precision joint platform with 48 N·m peak joint torque
• RL-based reinforcement learning gait control for autonomous all-terrain locomotion
• SDK development kit with Ethernet, USB, SBUS, UART, and power expansion interfaces
• URDF modeling support for simulation testing in Isaac Sim and MuJoCo environments
• Up to 3.5 m/s running speed, 8 kg payload capacity, and 1 to 2 hour battery endurance

AgiBot D1 EDU Quadruped Robot Dog (Development Edition) is a compact and agile quadruped robot platform developed by AGIBOT for research, education, and custom application development. Sharing the same high-performance chassis as the D1 Pro, it adds a full SDK development kit and a suite of standardized expansion interfaces, opening the platform to payload integration and secondary development. Its reinforcement learning-based gait controller navigates diverse terrain conditions autonomously, while supporting a full range of dynamic actions including jumps, backflips, and bipedal standing. The EDU is designed for teams and institutions that require both a capable mobile platform and the flexibility to build custom behaviors and attach third-party sensor payloads.

The platform is compatible with a broad ecosystem of development modules including LiDAR, depth cameras, RTK modules, 4G/5G connectivity, and image transmission hardware, all accessible through its open interface architecture. URDF modeling support allows developers to test and validate locomotion algorithms directly within Isaac Sim and MuJoCo simulation environments before deploying to hardware. Real-time image transmission and handheld remote control remain standard, enabling supervised operation alongside autonomous or semi-autonomous development workflows. As an AI robotics platform built for the research and education sector, the D1 EDU serves robotics labs, university programs, and developer teams seeking a reliable quadruped foundation for algorithm development, multi-sensor integration, and applied autonomous systems research.

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Model Features

The D1 EDU combines the same high-performance chassis as the D1 Pro with an open development layer designed for researchers, educators, and engineers.

Robust
Mobility →

Robust
Mobility

All-Terrain Performance

Max Speed: 3.7 m/s

Stair Climb: Up to 16 cm

Jump Height: Up to 35 cm

Max Slope: 30°

Self-Balancing: Supported

High-Torque
Motors →

High-Torque
Motors

Peak Joint Torque: 48 N·m

Precision Joint Motors: 12

Encoder: Dual encoder

Zero Calibration: Not required

Flexible
Development →

Flexible
Development

Open SDK

Interfaces: Ethernet, USB, SBUS, UART, Power

Simulation: Isaac Sim, MuJoCo (URDF)

Compatible Modules: LiDAR, Depth Camera, RTK, 4G/5G

Secondary Dev: Supported

Use Cases & Application Scenarios

From university robotics labs to field sensor integration and live demonstrations, the D1 EDU is built to support the full range of research and applied development workflows.

Robotics Research & Algorithm Development

The D1 EDU provides researchers with a capable and open quadruped platform for developing and validating locomotion algorithms, sensor fusion approaches, and autonomous navigation systems. URDF support and compatibility with Isaac Sim and MuJoCo allow teams to iterate in simulation before deploying directly to hardware, reducing development cycles and hardware risk.

Multi-Sensor Payload Integration

Standardized expansion interfaces make the D1 EDU a practical foundation for mounting and integrating third-party sensors and communication modules, including LiDAR, depth cameras, RTK positioning, and 4G/5G connectivity. Teams working on inspection, mapping, or data collection applications can configure the platform to match specific mission requirements without custom hardware modifications.

Education & Curriculum Integration

University programs and technical institutes can use the D1 EDU as a hands-on learning platform for courses covering embedded systems, reinforcement learning, and mobile robotics. Its accessible SDK and real-world dynamic behaviors provide students with a meaningful development environment that bridges classroom theory and practical deployment.