19 research outputs found
Task-embedded control networks for few-shot imitation learning
Much like humans, robots should have the ability to leverage knowledge from previously learned tasks in order to learn new tasks quickly in new and unfamiliar environments. Despite this, most robot learning approaches have focused on learning a single task, from scratch, with a limited notion of generalisation, and no way of leveraging the knowledge to learn other tasks more efficiently. One possible solution is meta-learning, but many of the related approaches are limited in their ability to scale to a large number of tasks and to learn further tasks without forgetting previously learned ones. With this in mind, we introduce Task-Embedded Control Networks, which employ ideas from metric learning in order to create a task embedding that can be used by a robot to learn new tasks from one or more demonstrations. In the area of visually-guided manipulation, we present simulation results in which we surpass the performance of a state-of-the-art method when using only visual information from each demonstration. Additionally, we demonstrate that our approach can also be used in conjunction with domain randomisation to train our few-shot learning ability in simulation and then deploy in the real world without any additional training. Once deployed, the robot can learn new tasks from a single real-world demonstration
Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer
Domain randomisation is a very popular method for visual sim-to-real transfer
in robotics, due to its simplicity and ability to achieve transfer without any
real-world images at all. Nonetheless, a number of design choices must be made
to achieve optimal transfer. In this paper, we perform a comprehensive
benchmarking study on these different choices, with two key experiments
evaluated on a real-world object pose estimation task. First, we study the
rendering quality, and find that a small number of high-quality images is
superior to a large number of low-quality images. Second, we study the type of
randomisation, and find that both distractors and textures are important for
generalisation to novel environments.Comment: The paper has been accepted to be published in ICRA 202
Learning to Interactively Learn and Assist
When deploying autonomous agents in the real world, we need effective ways of
communicating objectives to them. Traditional skill learning has revolved
around reinforcement and imitation learning, each with rigid constraints on the
format of information exchanged between the human and the agent. While scalar
rewards carry little information, demonstrations require significant effort to
provide and may carry more information than is necessary. Furthermore, rewards
and demonstrations are often defined and collected before training begins, when
the human is most uncertain about what information would help the agent. In
contrast, when humans communicate objectives with each other, they make use of
a large vocabulary of informative behaviors, including non-verbal
communication, and often communicate throughout learning, responding to
observed behavior. In this way, humans communicate intent with minimal effort.
In this paper, we propose such interactive learning as an alternative to reward
or demonstration-driven learning. To accomplish this, we introduce a
multi-agent training framework that enables an agent to learn from another
agent who knows the current task. Through a series of experiments, we
demonstrate the emergence of a variety of interactive learning behaviors,
including information-sharing, information-seeking, and question-answering.
Most importantly, we find that our approach produces an agent that is capable
of learning interactively from a human user, without a set of explicit
demonstrations or a reward function, and achieving significantly better
performance cooperatively with a human than a human performing the task alone.Comment: AAAI 2020. Video overview at https://youtu.be/8yBvDBuAPrw, paper
website with videos and interactive game at
http://interactive-learning.github.io