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Found 4 papers, 1 papers with code
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.
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks.
RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking
The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets.
Spatial-Language Attention Policies for Efficient Robot Learning
We see a 4x improvement over baseline in mobile manipulation setting.
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