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Found 6 papers, 5 papers with code
Behavior Generation with Latent Actions
Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction.
OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics
The results demonstrate that OK-Robot achieves a 58. 5% success rate in open-ended pick-and-drop tasks, representing a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM) with nearly 1. 8x the performance of prior work.
On Bringing Robots Home
We use the Stick to collect 13 hours of data in 22 homes of New York City, and train Home Pretrained Representations (HPR).
From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data
While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging.
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization.
Behavior Transformers: Cloning $k$ modes with one stone
In this work, we present Behavior Transformer (BeT), a new technique to model unlabeled demonstration data with multiple modes.
