Rt-h: Action hierarchies using language
Language provides a way to break down complex concepts into digestible pieces. Recent
works in robot imitation learning use language-conditioned policies that predict actions …
works in robot imitation learning use language-conditioned policies that predict actions …
Concept2robot: Learning manipulation concepts from instructions and human demonstrations
We aim to endow a robot with the ability to learn manipulation concepts that link natural
language instructions to motor skills. Our goal is to learn a single multi-task policy that takes …
language instructions to motor skills. Our goal is to learn a single multi-task policy that takes …
Language conditioned imitation learning over unstructured data
C Lynch, P Sermanet - arXiv preprint arXiv:2005.07648, 2020 - arxiv.org
Natural language is perhaps the most flexible and intuitive way for humans to communicate
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …
Hydra: Hybrid robot actions for imitation learning
Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert
demonstrations. However, policies learned through IL suffer from state distribution shift at …
demonstrations. However, policies learned through IL suffer from state distribution shift at …
Any-point trajectory modeling for policy learning
Learning from demonstration is a powerful method for teaching robots new skills, and more
demonstration data often improves policy learning. However, the high cost of collecting …
demonstration data often improves policy learning. However, the high cost of collecting …
[PDF][PDF] Grounding language in play
C Lynch, P Sermanet - arXiv preprint arXiv:2005.07648, 2020 - academia.edu
Natural language is perhaps the most versatile and intuitive way for humans to communicate
tasks to a robot. Prior work on Learning from Play (LfP)(Lynch et al., 2019) provides a simple …
tasks to a robot. Prior work on Learning from Play (LfP)(Lynch et al., 2019) provides a simple …
Using both demonstrations and language instructions to efficiently learn robotic tasks
Demonstrations and natural language instructions are two common ways to specify and
teach robots novel tasks. However, for many complex tasks, a demonstration or language …
teach robots novel tasks. However, for many complex tasks, a demonstration or language …
Implicit kinematic policies: Unifying joint and cartesian action spaces in end-to-end robot learning
Action representation is an important yet often overlooked aspect in end-to-end robot
learning with deep networks. Choosing one action space over another (eg target joint …
learning with deep networks. Choosing one action space over another (eg target joint …
Learning by watching: Physical imitation of manipulation skills from human videos
Learning from visual data opens the potential to accrue a large range of manipulation
behaviors by leveraging human demonstrations without specifying each of them mathe …
behaviors by leveraging human demonstrations without specifying each of them mathe …
Language-conditioned imitation learning for robot manipulation tasks
Imitation learning is a popular approach for teaching motor skills to robots. However, most
approaches focus on extracting policy parameters from execution traces alone (ie, motion …
approaches focus on extracting policy parameters from execution traces alone (ie, motion …