Towards self-adaptable robots: from programming to training machines
This addresses the problem of robot adaptability and development efficiency for robotics and AI researchers, though it appears incremental as it builds on existing modular hardware and training techniques.
The paper tackles the challenge of building adaptable robots by introducing a self-adaptable robot concept that uses hardware modularity and AI training instead of programming, demonstrating in simulation and on a real robot that this approach reduces build time/effort and produces fast-generalizing, noise-robust behaviors.
We argue that hardware modularity plays a key role in the convergence of Robotics and Artificial Intelligence (AI). We introduce a new approach for building robots that leads to more adaptable and capable machines. We present the concept of a self-adaptable robot that makes use of hardware modularity and AI techniques to reduce the effort and time required to be built. We demonstrate in simulation and with a real robot how, rather than programming, training produces behaviors in the robot that generalize fast and produce robust outputs in the presence of noise. In particular, we advocate for mammals.