Multi-Task Autonomy For Robotics
Creators: name: Bian, Xihan
Publisher: name: University of Surrey
Year: 2023
Subjects: Reinforcement Learning
Imitation Learning
Multi-task Learning
Generalization
Robotics

description: Robotic technology is integrating into more aspects of our society. The ability to perform
description: multiple different functions in various environments is an important research challenge. Recent
description: developments in multi-task learning in Artificial Intelligence (AI) and machine learning could
description: enable a new generation of robotic technology. This thesis aims to explore techniques which
description: combine AI with robotics to enable generalization across a range of environments.<br>
description: <br>
description: As the level of abstraction possible in deep learning has grown a very promising avenue of
description: research has become feasible: hybrid systems. In these approaches, an AI system is used to
description: undertake high-level planning and strategisation, while the execution of the plans is undertaken
description: by classical non-learned approaches. This enables a mutual compensation of AI and traditional
description: robotic systems, as the AI agents excel at generalisation for high-level problems and longterm
description: goal-oriented planning, while the robotics techniques specialise in precise and repeatable operations [24].
description: This thesis attempts to build upon this and further explore the multi-task-enabled AI agent and its
description: potential in robotic tasks.<br>
description: <br>
description: First, general-purpose robots need to be able to work in multiple different environments. Even
description: when performing similar tasks, different behaviour should be deployed to best fit the current
description: environment. In this thesis, we propose a new approach to navigation, where it is treated
description: as a multi-task learning problem. This enables the robot to learn to behave differently in
description: visual navigation tasks for different environments while also learning shared expertise across
description: environments. We evaluate our approach in both simulated environments as well as real-world
description: data.<br>
description: <br>
description: Following this, we introduce a new perspective for learning transferable content in multi-task
description: imitation learning. Humans are able to transfer skills and knowledge to new tasks. If we can
description: cycle to work and drive to the store, we can also cycle to the store and drive to work. We take
description: inspiration from this and hypothesize the latent memory of a policy network can be disentangled
description: into two partitions. These contain either the knowledge of the environmental context for the task
description: or the generalizable skill needed to solve the task. This allows improved training efficiency and
description: better generalization over previously unseen combinations of skills in the same environment, and
description: the same task in unseen environments. We used the proposed approach to train a disentangled
description: agent for two different multi-task IL environments. In both cases, we outperformed the SOTA
description: by 30% in task success rate. We also demonstrated this for navigation on a real robot, including
description: the visual navigation task of the previous chapter. This demonstration was also ported to a real
description: robot.<br>
description: <br>
description: This ability to generalize to previously unseen tasks with little to no supervision is a key
description: challenge in modern machine learning research. Researchers often rely on reinforcement and
description: imitation learning to provide online adaptation to new tasks, through trial and error learning.
description: However, this can be challenging for complex tasks which require many timesteps or large
description: numbers of subtasks to complete. These “long horizon” tasks suffer from sample inefficiency
description: and can require extremely long training times before the agent can learn to perform the necessary
description: long-term planning. Therefore, we finally introduce CASE which attempts to address these
description: issues by utilising adaptive “near future” subgoals. These subgoals are re-calculated at each step
description: using compositional arithmetic in a learned latent representation space similar to the previous
description: chapter. In addition to improving learning efficiency for standard long-term tasks, this approach
description: also makes it possible to perform one-shot generalization to previously unseen tasks, given only
description: a single reference trajectory for the task in a different environment.<br>
description: <br>
description: When we combine the works in this thesis, these contributions provide insights for a better
description: multitask generalization framework for long and complex robotic planning. The contributions centre
description: on multi-task learning and crossover to robotic navigation, reinforcement/imitation learning,
description: and generalization. The experiments in this thesis demonstrate that an AI-controlled robot can
description: perform simple tasks as well as a non-AI robot, while also handling more difficult tasks. This
description: thesis presents a general direction for robotic AI capable of handling multiple complex tasks and
description: sequential generalization. This enables better autonomy in both AI and robotics in the future.<br>