90 research outputs found
Teaching Robots Novel Objects by Pointing at Them
Robots that must operate in novel environments and collaborate with humans
must be capable of acquiring new knowledge from human experts during operation.
We propose teaching a robot novel objects it has not encountered before by
pointing a hand at the new object of interest. An end-to-end neural network is
used to attend to the novel object of interest indicated by the pointing hand
and then to localize the object in new scenes. In order to attend to the novel
object indicated by the pointing hand, we propose a spatial attention
modulation mechanism that learns to focus on the highlighted object while
ignoring the other objects in the scene. We show that a robot arm can
manipulate novel objects that are highlighted by pointing a hand at them. We
also evaluate the performance of the proposed architecture on a synthetic
dataset constructed using emojis and on a real-world dataset of common objects
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
Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach
We present a robot eye-hand coordination learning method that can directly
learn visual task specification by watching human demonstrations. Task
specification is represented as a task function, which is learned using inverse
reinforcement learning(IRL) by inferring differential rewards between state
changes. The learned task function is then used as continuous feedbacks in an
uncalibrated visual servoing(UVS) controller designed for the execution phase.
Our proposed method can directly learn from raw videos, which removes the need
for hand-engineered task specification. It can also provide task
interpretability by directly approximating the task function. Besides,
benefiting from the use of a traditional UVS controller, our training process
is efficient and the learned policy is independent from a particular robot
platform. Various experiments were designed to show that, for a certain DOF
task, our method can adapt to task/environment variances in target positions,
backgrounds, illuminations, and occlusions without prior retraining.Comment: Accepted in ICRA 201
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