Recent advances in robot learning from demonstration
H Ravichandar, AS Polydoros… - Annual review of …, 2020 - annualreviews.org
In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
Theseus: A library for differentiable nonlinear optimization
We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …
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 …
Kernelized movement primitives
Imitation learning has been studied widely as a convenient way to transfer human skills to
robots. This learning approach is aimed at extracting relevant motion patterns from human …
robots. This learning approach is aimed at extracting relevant motion patterns from human …
Continuous-time Gaussian process motion planning via probabilistic inference
We introduce a novel formulation of motion planning, for continuous-time trajectories, as
probabilistic inference. We first show how smooth continuous-time trajectories can be …
probabilistic inference. We first show how smooth continuous-time trajectories can be …
Motion planning diffusion: Learning and planning of robot motions with diffusion models
Learning priors on trajectory distributions can help accelerate robot motion planning
optimization. Given previously successful plans, learning trajectory generative models as …
optimization. Given previously successful plans, learning trajectory generative models as …
[图书][B] Learning to learn with gradients
CB Finn - 2018 - search.proquest.com
Humans have a remarkable ability to learn new concepts from only a few examples and
quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience …
quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience …
Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation
Many Reinforcement Learning (RL) approaches use joint control signals (positions,
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
Applied Affective Computing
Affective computing is a nascent field situated at the intersection of artificial intelligence with
social and behavioral science. It studies how human emotions are perceived and …
social and behavioral science. It studies how human emotions are perceived and …
Federated imitation learning: A novel framework for cloud robotic systems with heterogeneous sensor data
Humans are capable of learning a new behavior by observing others to perform the skill.
Similarly, robots can also implement this by imitation learning. Furthermore, if with external …
Similarly, robots can also implement this by imitation learning. Furthermore, if with external …