An overview of machine teaching
In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …
presented as varying along a dimension. The collection of dimensions then form the …
What matters in learning from offline human demonstrations for robot manipulation
Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …
manipulation capabilities. While recent advances have been made in imitation learning and …
Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training
Conveying complex objectives to reinforcement learning (RL) agents can often be difficult,
involving meticulous design of reward functions that are sufficiently informative yet easy …
involving meticulous design of reward functions that are sufficiently informative yet easy …
[HTML][HTML] Reinforcement learning with human advice: a survey
In this paper, we provide an overview of the existing methods for integrating human advice
into a Reinforcement Learning process. We first propose a taxonomy of the different forms of …
into a Reinforcement Learning process. We first propose a taxonomy of the different forms of …
One-shot imitation from observing humans via domain-adaptive meta-learning
Humans and animals are capable of learning a new behavior by observing others perform
the skill just once. We consider the problem of allowing a robot to do the same--learning …
the skill just once. We consider the problem of allowing a robot to do the same--learning …
State entropy maximization with random encoders for efficient exploration
Recent exploration methods have proven to be a recipe for improving sample-efficiency in
deep reinforcement learning (RL). However, efficient exploration in high-dimensional …
deep reinforcement learning (RL). However, efficient exploration in high-dimensional …
Better-than-demonstrator imitation learning via automatically-ranked demonstrations
The performance of imitation learning is typically upper-bounded by the performance of the
demonstrator. While recent empirical results demonstrate that ranked demonstrations allow …
demonstrator. While recent empirical results demonstrate that ranked demonstrations allow …
[LIVRE][B] Robot learning from human teachers
S Chernova, AL Thomaz - 2022 - books.google.com
Learning from Demonstration (LfD) explores techniques for learning a task policy from
examples provided by a human teacher. The field of LfD has grown into an extensive body …
examples provided by a human teacher. The field of LfD has grown into an extensive body …
Avid: Learning multi-stage tasks via pixel-level translation of human videos
Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex
behaviors through experience. However, realizing this promise for long-horizon tasks in the …
behaviors through experience. However, realizing this promise for long-horizon tasks in the …
Human-robot mutual adaptation in collaborative tasks: Models and experiments
Adaptation is critical for effective team collaboration. This paper introduces a computational
formalism for mutual adaptation between a robot and a human in collaborative tasks. We …
formalism for mutual adaptation between a robot and a human in collaborative tasks. We …