Modular and Lifelong Learning for Developing General Purpose Robotic Agents

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Reinforcement learning or learning from interaction is a promising route to generalists robots that can learn and improve their performance with additional training. The long-term objective (10-15 years) of this research program is to address two limitations in applying learning from interaction methods on real robotic agents via (1) learning algorithms to enable efficient training in the real world and (2) develop principled general purpose reward functions to encourage robots to learn diverse behaviours similar to humans. This research is directed by solutions that are not overly influenced by simulation and aims to target methods that work on real robots in the real world to avoid potential bias. These real-world assumptions introduce additional constraints on the learning algorithms. However, these constraints induce a more independent learning system with more human-like learning capabilities. This research program has three key short-term (5 year) objectives that are hurdles limiting the (RL) paradigm from producing agents with human-level skill. Aim 1 Build algorithms that enable generalization across MDPs and morphologies by learning modular and reusable decision making processes. Aim 2: Develop continual learning systems to enable agents that can functionally and independently train in the real-world. Aim 3: Create general and principled reward functions that enable agents to learn diverse skills that are useful for future tasks.