Continual Learning for Autonomous Robots: A Prototype-based Approach
Elvin Hajizada Balachandran Swaminathan Yulia Sandamirskaya
Humans and animals learn throughout their lives from limited amounts of
sensed data, both with and without supervision. Autonomous, intelligent robots
of the future are often expected to do the same. The existing continual
learning (CL) methods are usually not directly applicable to robotic settings:
they typically require buffering and a balanced replay of training data. A
few-shot online continual learning (FS-OCL) setting has been proposed to
address more realistic scenarios where robots must learn from a non-repeated
sparse data stream. To enable truly autonomous life-long learning, an
additional challenge of detecting novelties and learning new items without
supervision needs to be addressed. We address this challenge with our new
prototype-based approach called Continually Learning Prototypes (CLP). In
addition to being capable of FS-OCL learning, CLP also detects novel objects
and learns them without supervision. To mitigate forgetting, CLP utilizes a
novel metaplasticity mechanism that adapts the learning rate individually per
prototype. CLP is rehearsal-free, hence does not require a memory buffer, and
is compatible with neuromorphic hardware, characterized by ultra-low power
consumption, real-time processing abilities, and on-chip learning. Indeed, we
have open-sourced a simple version of CLP in the neuromorphic software
framework Lava, targetting Intel's neuromorphic chip Loihi 2. We evaluate CLP
on a robotic vision dataset, OpenLORIS. In a low-instance FS-OCL scenario, CLP
shows state-of-the-art results. In the open world, CLP detects novelties with
superior precision and recall and learns features of the detected novel classes
without supervision, achieving a strong baseline of 99% base class and 65%/76%
(5-shot/10-shot) novel class accuracy.