Robots learn human touch with less data using adaptive motion system

Researchers at Keio University in Japan have developed an adaptive motion reproduction system that enables robots to perform human-like grasping and manipulation using minimal training data. Traditional robotic systems struggle to adjust when objects vary in weight, stiffness, or texture, limiting their use to controlled factory environments. The new system leverages Gaussian process regression to model complex nonlinear relationships between object properties and human-applied forces, allowing robots to infer human motion intent and adapt their movements to unfamiliar objects in dynamic, real-world settings such as homes and hospitals. Testing showed that this approach significantly outperforms conventional motion reproduction and imitation learning methods, reducing position and force errors by substantial margins both within and beyond the training data range. By requiring less data and lowering machine learning costs, the technology has broad potential applications, including life-support robots that must adapt to diverse tasks. This advancement builds on Keio University’s expertise in force-tactile feedback and haptic technologies and represents a key step toward enabling robots to operate reliably in unpredictable environments. The