In recent years, China has made rapid progress in robotics, with one notable research direction involving training robots through simulated ‘fights’ to enhance their impact resistance and environmental adaptability. This approach does not involve actual violence; rather, it uses high-precision sensors, dynamic control algorithms, and reinforcement learning models to enable robots to quickly adjust posture, maintain balance, and continue tasks when subjected to external forces or collisions.For instance, bipedal or quadruped robots developed by universities and research institutes undergo tests involving pushes, impacts, and even intentional falls from various directions. Through repeated ‘getting hit’ training, these robots learn to recognize types of disturbances and optimize their movement strategies in real time, significantly improving robustness in complex and unpredictable environments. This capability is crucial for real-world applications such as search-and-rescue, security, and logistics.Moreover, this research fosters deeper integration between artificial intelligence and robotics. Simulated adversarial environments allow researchers to efficiently evaluate a robot’s perception, decision-making, and execution capabilities, accelerating the transition from lab prototypes to practical deployment. Although still largely experimental, the concept of ‘training to take hits’ is emerging as a key strategy in China’s pursuit of more resilient robotic systems.
近年来，中国在机器人技术领域快速发展，其中一项引人注目的研究方向是让机器人通过模拟“打架”来提升其抗冲击能力和环境适应性。这种训练并非真正意义上的暴力对抗，而是通过高精度传感器、动态控制算法和强化学习模型，使机器人在受外力干扰或碰撞时能够迅速调整姿态、保持平衡并继续执行任务。例如，一些高校和科研机构开发的双足或四足机器人，在实验中会接受来自不同方向的推搡、撞击甚至跌倒测试。通过反复“挨打”训练，机器人学会识别外部扰动类型，并实时优化自身动作策略，从而提高在复杂、不确定环境中的鲁棒性。这种技术对救援、安防、物流等实际应用场景具有重要意义。此外，这类研究也推动了人工智能与机器人学的深度融合。通过模拟对抗环境，研究人员可以更高效地测试机器人的感知、决策与执行能力，加速其从实验室走向现实世界。尽管目前仍处于实验阶段，但“练挨打”的理念正逐渐成为中国机器人研发中提升系统韧性的关键路径之一。
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