Dobot Rover X1 Explorer Quadruped Robot

• Dobot Rover X1 Explorer Quadruped Robot
• Bio-inspired point-legged quadruped for all-terrain navigation
• Dual front and rear 1080p RGB cameras plus dual built-in depth cameras for bidirectional perception
• 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
• Modular mounting interfaces support multiple sensors, computing units, and research payloads

The Dobot Rover X1 Explorer Quadruped Robot is an education-focused, bio-inspired quadruped platform developed by DOBOT Robotics, designed to serve as an integrated teaching and research tool for learners at every level. Built on an inverted knee-joint architecture, the platform's legged locomotion enables it to handle diverse terrain types, from flat indoor floors to 30- to 35-degree inclines, and obstacles up to 16 cm in height. Front and rear 1080p RGB cameras combined with dual built-in depth cameras provide omnidirectional environmental awareness, while an intelligent obstacle-avoidance system delivers bidirectional auto-braking responses during autonomous operation. The Explorer is purpose-built for K-12 classrooms, vocational training programs, undergraduate laboratories, and research institutions that need a single hardware platform capable of growing alongside the complexity of instruction.

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. Autonomous following, AI voice interaction, Wi-Fi and 4G connectivity, and OTA update support make the robot suitable for deployment across connected campus and laboratory environments without significant infrastructure requirements. An open development ecosystem built around C++ and Python SDKs and the Isaac Gym simulation environment allows educators and developers to customize curriculum, reproduce experiments, and validate reinforcement learning algorithms directly on the hardware. With collision protection mechanisms and a modular payload interface, the Explorer is a long-term AI robotics platform that bridges hands-on STEM education and serious robotics 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 (front and rear)

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 Module available on Vision and Sentinel models (40 TOPS, 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

A cross-disciplinary platform that scales with the learner. Through graphical programming and direct app, voice, and controller interaction, beginners build task logic and see their code execute on a physical robot in real time — then progress into undergraduate control theory and research-grade work in reinforcement learning, dynamics modeling, and gait control, all running directly on the hardware. A single robot serves an entire curriculum as instruction deepens, from a first working program to advanced algorithm development.

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.