Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Li, Mengxi"'
Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community. While the current state-of-the-art methods with reinforcement learning (RL) show promising performance in acquiring manipula
Externí odkaz:
http://arxiv.org/abs/2212.00955
When humans perform contact-rich manipulation tasks, customized tools are often necessary to simplify the task. For instance, we use various utensils for handling food, such as knives, forks and spoons. Similarly, robots may benefit from specialized
Externí odkaz:
http://arxiv.org/abs/2211.02201
Publikováno v:
CoRL 2021
The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations. Prior works on learning from demonstrations assume that the demonstrations are collected by a demonstrator that has
Externí odkaz:
http://arxiv.org/abs/2110.15142
Autor:
Losey, Dylan P., Jeon, Hong Jun, Li, Mengxi, Srinivasan, Krishnan, Mandlekar, Ajay, Garg, Animesh, Bohg, Jeannette, Sadigh, Dorsa
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use to control their robots are low-dimensional. Consider teleoperating a
Externí odkaz:
http://arxiv.org/abs/2107.02907
When personal, assistive, and interactive robots make mistakes, humans naturally and intuitively correct those mistakes through physical interaction. In simple situations, one correction is sufficient to convey what the human wants. But when humans a
Externí odkaz:
http://arxiv.org/abs/2104.00078
When humans control drones, cars, and robots, we often have some preconceived notion of how our inputs should make the system behave. Existing approaches to teleoperation typically assume a one-size-fits-all approach, where the designers pre-define a
Externí odkaz:
http://arxiv.org/abs/2007.11627
When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpreting our partner's actions is typically difficult, since a given acti
Externí odkaz:
http://arxiv.org/abs/1910.07613
The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations
Externí odkaz:
http://arxiv.org/abs/1711.00258