The Booster K1 is a compact humanoid robot platform from Booster Robotics featuring 22 degrees of freedom for research, education, and development applications. Standing 95 cm tall and weighing 19.5 kg, the K1 provides a capable bipedal system at a scale suitable for laboratory environments, classrooms, and proof-of-concept work—offering an accessible entry point into full-scale humanoid robotics.
The K1’s joint configuration is thoughtfully designed for expressive, dynamic motion. With 2 DOF in the head/neck, 4 DOF per arm, and 6 DOF per leg, the robot achieves stable bipedal locomotion while maintaining the ability to perform upper-body gestures and head tracking. This distribution enables researchers to explore the full spectrum of humanoid motion challenges, from balance and gait optimization to human-robot interaction and gestural communication.
Onboard computing delivers 117 TOPS of GPU processing power, providing substantial capability for AI model deployment, computer vision applications, and real-time control algorithms. This computing headroom supports reinforcement learning experiments, neural network inference, sensor fusion, and the computationally demanding tasks inherent in humanoid robot development. Secondary development is fully supported, giving teams flexibility to implement custom behaviors and integrate the K1 into broader research frameworks.
The K1 serves universities, research laboratories, and industrial R&D teams exploring bipedal locomotion, human-robot interaction, embodied AI, and service robotics concepts. Its compact footprint makes it practical for indoor testing environments while still demonstrating the dynamics, control challenges, and interaction patterns of legged humanoid systems. The robot bridges the gap between small educational humanoids and full-scale industrial platforms.
Whether used for gait research, AI development, HRI studies, or educational demonstrations, the Booster K1 provides a serious humanoid platform without the space requirements, safety complexity, or cost of larger systems. It’s designed for teams who need real bipedal hardware to validate algorithms, train students, or prototype service robot concepts before scaling up.
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