Rt-1: Robotics transformer for real-world control at scale
A Brohan, N Brown, J Carbajal, Y Chebotar… - arXiv preprint arXiv …, 2022 - arxiv.org
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …
learning models can solve specific downstream tasks either zero-shot or with small task …
A survey of meta-reinforcement learning
While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …
machine learning, it is held back from more widespread adoption by its often poor data …
Mt-opt: Continuous multi-task robotic reinforcement learning at scale
General-purpose robotic systems must master a large repertoire of diverse skills to be useful
in a range of daily tasks. While reinforcement learning provides a powerful framework for …
in a range of daily tasks. While reinforcement learning provides a powerful framework for …
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 …
Learning generalizable robotic reward functions from" in-the-wild" human videos
We are motivated by the goal of generalist robots that can complete a wide range of tasks
across many environments. Critical to this is the robot's ability to acquire some metric of task …
across many environments. Critical to this is the robot's ability to acquire some metric of task …
Skilldiffuser: Interpretable hierarchical planning via skill abstractions in diffusion-based task execution
Diffusion models have demonstrated strong potential for robotic trajectory planning.
However generating coherent trajectories from high-level instructions remains challenging …
However generating coherent trajectories from high-level instructions remains challenging …
Deep reinforcement learning from demonstrations to assist service restoration in islanded microgrids
Microgrids can be operated in island mode during utility grid outages to support service
restoration and improve system resilience. To schedule and dispatch distributed energy …
restoration and improve system resilience. To schedule and dispatch distributed energy …
Imitation learning by estimating expertise of demonstrators
Many existing imitation learning datasets are collected from multiple demonstrators, each
with different expertise at different parts of the environment. Yet, standard imitation learning …
with different expertise at different parts of the environment. Yet, standard imitation learning …
Towards more generalizable one-shot visual imitation learning
A general-purpose robot should be able to master a wide range of tasks and quickly learn a
novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches …
novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches …
Aligning robot and human representations
To act in the world, robots rely on a representation of salient task aspects: for example, to
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …