12 citations · 25 across the 3 of their papers we have counts for
5 papers · 1 filter
FAST: Efficient Action Tokenization for Vision-Language-Action Models
Karl Pertsch, Kyle Stachowicz, Brian Ichter +6
Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behav…
Open-vocabulary Queryable Scene Representations for Real World Planning
Boyuan Chen, Fei Xia, Brian Ichter +5
Large language models (LLMs) have unlocked new capabilities of task planning from human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are limited…
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
Suraj Nair, Eric Mitchell, Kevin Chen +3
We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and eff…
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
Brian Ichter, Pierre Sermanet, Corey Lynch
Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planni…
Learning Object-conditioned Exploration using Distributed Soft Actor Critic
Ayzaan Wahid, Austin Stone, Kevin Chen +2
Object navigation is defined as navigating to an object of a given label in a complex, unexplored environment. In its general form, this problem poses several challenges for Roboti…