Dobot Rover X1 Explorer Vision Quadruped Robot

• Dobot Rover X1 Explorer Vision Quadruped Robot
• Bio-inspired point-legged quadruped with 40 TOPS onboard AI compute for advanced perception tasks
• Dual built-in depth cameras plus Intel RealSense D435i for enhanced three-dimensional environmental mapping
• C++ and Python SDK with open low-level and high-level control APIs for secondary development
• Isaac Gym simulation environment support for reinforcement learning and motion research
• Inverted knee joint architecture enhances stair climbing and performance across complex terrain
• Extended 8000 mAh battery delivers approximately 2.5 hours of continuous runtime

Dobot Rover X1 Explorer Vision Quadruped Robot is an education-focused, bio-inspired quadruped platform developed by DOBOT Robotics, built for institutions that require onboard AI compute alongside advanced depth perception. Powered by a 40 TOPS Orin Nano module, the Explorer Vision adds dedicated edge inference capability to the same inverted knee joint architecture that handles terrain ranging from flat indoor floors to 30 to 35-degree inclines and obstacles up to 16 cm in height. Its sensor suite combines front and rear 1080p RGB cameras, dual built-in depth cameras, and an Intel RealSense D435i, giving the platform a richer three-dimensional picture of its surroundings compared to the base Explorer. This expanded perception stack is designed for research teams and institutions developing computer-vision models, sensor fusion pipelines, and machine-perception algorithms that demand higher spatial fidelity.

The platform adopts a layered software interface philosophy, exposing high-level motion control APIs for introductory coursework alongside raw IMU data, joint torque, and MIT-style position-velocity control for advanced research applications. An Enhanced Camera SDK unlocks the full capability of the RealSense D435i alongside the built-in depth cameras, while dual-language AI voice interaction broadens deployment options across multilingual educational environments. Wi-Fi and 4G connectivity, OTA update support, and an open development ecosystem built around C++ and Python SDKs and the Isaac Gym simulation environment allow educators and researchers to customize curriculum, reproduce experiments, and validate algorithms directly on hardware. With an extended 8000 mAh battery, collision protection mechanisms, and a modular payload interface, the Explorer Vision is a capable long-term AI robotics platform for institutions ready to move beyond introductory robotics into applied perception and autonomy research.

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Model Features

Key hardware and engineering differentiators of the Explorer platform.

Mobility & Gait →

Mobility & Gait

Architecture: Inverted knee joint for enhanced stair climbing and confined-space navigation

Max Speed: 1.8 m/s

Climbing: 30 to 35-degree slopes

Obstacle Clearance: Up to 16 cm

Motion Actions: Waving, Jumping, Leaping

Sensing & Perception →

Sensing & Perception

RGB Cameras: Front and rear 1080P (1920x1080 at 30fps)

Depth Cameras: Dual built-in depth cameras + Intel RealSense D435i

Obstacle Avoidance: Bidirectional with auto-braking response

Autonomous Following: Supported

AI Visual Tracking: Supported

Computing & SDK →

Computing & SDK

SDK: C++ and Python with open low-level and high-level control APIs

Simulation: Isaac Gym environment provided

Secondary Development: Supported

Connectivity: Wi-Fi, 4G, Bluetooth, App control

OTA Updates: Supported

AI Computing: 40 TOPS onboard compute (Orin Nano)

Capabilities

Autonomous Following

The Explorer supports autonomous following and intelligent obstacle avoidance for untethered operation. Bidirectional detection with auto-braking ensures safe operation in dynamic environments shared with people.

Open SDK Support

C++ and Python SDKs expose both high-level motion control and low-level joint interfaces, enabling seamless integration with research workflows. Open APIs cover speed control, gait switching, raw IMU data, and MIT-style joint torque commands.

Isaac Gym Simulation

An Isaac Gym simulation environment is provided out of the box, enabling reinforcement learning experiments and motion algorithm validation before deployment on the physical robot. Motion and navigation application examples are also included to accelerate research workflows.

Modular Payload and Sensor Expansion

A modular camera mount and multiple expansion interfaces allow researchers to attach additional sensors and custom payloads to the platform. This makes the Explorer compatible with a wide range of experimental configurations and development needs.

Use Cases & Application Scenarios

Teaching & Research — Applied AI & Perception

Building on the platform’s full teaching curriculum, the Vision configuration moves perception workloads onto the robot itself. With onboard AI computing and an integrated depth-sensing camera, students and researchers can develop, deploy, and run computer-vision and object-recognition models locally — and fuse visual and depth data for richer environmental understanding. This opens a higher research tier for laboratories whose coursework extends beyond motion control into applied AI, machine perception, and multi-sensor integration, building the groundwork for autonomy.

Interactive Engagement & Demonstration

A responsive, approachable robot for live settings. Combining visual following with AI voice interaction, the Explorer can act as an interactive demonstrator in classrooms, open houses, and exhibition spaces — giving students and visitors a tangible, conversational introduction to applied robotics and human-machine interaction.