Learning from few examples: A summary of approaches to few-shot learning
Learning fine-grained bimanual manipulation with low-cost hardware
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …
difficult for robots because they require precision, careful coordination of contact forces, and …
Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation
Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …
robotics. However, most results focus on table-top manipulation, lacking the mobility and …
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 …
[PDF][PDF] Vima: General robot manipulation with multimodal prompts
Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …
processing, where a single general-purpose language model can be instructed to perform …
How attentive are graph attention networks?
Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …
considered as the state-of-the-art architecture for representation learning with graphs. In …
Bc-z: Zero-shot task generalization with robotic imitation learning
In this paper, we study the problem of enabling a vision-based robotic manipulation system
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …
Prompting decision transformer for few-shot policy generalization
Human can leverage prior experience and learn novel tasks from a handful of
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …
How to train your robot with deep reinforcement learning: lessons we have learned
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …