Doctoral thesis

Slowness learning for curiosity-driven agents


154 p

Thèse de doctorat: Università della Svizzera italiana, 2014

English In the absence of external guidance, how can a robot learn to map the many raw pixels of high-dimensional visual inputs to useful action sequences? I study methods that achieve this by making robots self-motivated (curious) to continually build compact representations of sensory inputs that encode different aspects of the changing environment. Previous curiosity-based agents acquired skills by associating intrinsic rewards with world model improvements, and used reinforcement learning (RL) to learn how to get these intrinsic rewards. But unlike in previous implementations, I consider streams of high-dimensional visual inputs, where the world model is a set of compact low-dimensional representations of the high-dimensional inputs. To learn these representations, I use the slowness learning principle, which states that the underlying causes of the changing sensory inputs vary on a much slower time scale than the observed sensory inputs. The representations learned through the slowness learning principle are called slow features (SFs). Slow features have been shown to be useful for RL, since they capture the underlying transition process by extracting spatio-temporal regularities in the raw sensory inputs. However, existing techniques that learn slow features are not readily applicable to curiosity-driven online learning agents, as they estimate computationally expensive covariance matrices from the data via batch processing. The first contribution called the incremental SFA (IncSFA), is a low-complexity, online algorithm that extracts slow features without storing any input data or estimating costly covariance matrices, thereby making it suitable to be used for several online learning applications. However, IncSFA gradually forgets previously learned representations whenever the statistics of the input change. In open-ended online learning, it becomes essential to store learned representations to avoid re- learning previously learned inputs. The second contribution is an online active modular IncSFA algorithm called the curiosity-driven modular incremental slow feature analysis (Curious Dr. MISFA). Curious Dr. MISFA addresses the forgetting problem faced by IncSFA and learns expert slow feature abstractions in order from least to most costly, with theoretical guarantees. The third contribution uses the Curious Dr. MISFA algorithm in a continual curiosity-driven skill acquisition framework that enables robots to acquire, store, and re-use both abstractions and skills in an online and continual manner. I provide (a) a formal analysis of the working of the proposed algorithms; (b) compare them to the existing methods; and (c) use the iCub humanoid robot to demonstrate their application in real-world environments. These contributions together demonstrate that the online implementations of slowness learning make it suitable for an open-ended curiosity-driven RL agent to acquire a repertoire of skills that map the many raw pixels of high-dimensional images to multiple sets of action sequences.
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Computer science and technology
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