AI Enables Full-Body Object Manipulation by Robots

MIT uses reinforcement learning to enhance robots' full-body manipulations, overcoming computational challenges during contact.

Smoothing specific model equations enables robots to benefit from reinforcement learning without extensive trajectory calculations.

Potential applications include smaller, mobile robots for manufacturing and adaptable space exploration using onboard computers.

Efficient simulations show comparable performance and reduced computation time, validated in real-world tests on robotic arms.

Future improvements aim to extend the technique for dynamic motions, closing the performance gap between robots and humans in physical tasks.