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Found 7 papers, 2 papers with code
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
We introduce STRING: Separable Translationally Invariant Position Encodings.
Legal Evalutions and Challenges of Large Language Models
In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions.
RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation
Our method, RT-Affordance, is a hierarchical model that first proposes an affordance plan given the task language, and then conditions the policy on this affordance plan to perform manipulation.
RT-H: Action Hierarchies Using Language
Predicting these language motions as an intermediate step between tasks and actions forces the policy to learn the shared structure of low-level motions across seemingly disparate tasks.
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
Ranked #4 on
Robot Manipulation
on SimplerEnv-Google Robot
(using extra training data)
Interactive Language: Talking to Robots in Real Time
We present a framework for building interactive, real-time, natural language-instructable robots in the real world, and we open source related assets (dataset, environment, benchmark, and policies).
