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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Max Speed
1.8 m/s
Battery Life
2.5 hrs
Payload
3–7 kg
Weight
18.5 kg
Obstacle Clearance
16 cm
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)
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.
Dobot Rover X1 Explorer Quadruped Robot
Model: X1 Explorer Vision
•
Explorer Vision Bionic Quadruped Robot
•
Default Leg Modules (4 pc)
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Bluetooth Remote Controller
•
Charging Dock
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High-Capacity Battery Pack
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Intel RealSense D435i
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Enhanced Camera SDK
Package contents vary by configuration.
Dobot Rover X1 Explorer Quadruped Robot
Model: X1 Explorer Vision
Weight
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18.5 kg
Dimensions
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761 x 372 x 428 mm
Values shown are from the manufacturer's reference.
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