Search Results for author:
Found 7 papers, 4 papers with code
TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success.
THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions.
Ranked #1 on
Robot Manipulation Generalization
on The COLOSSEUM
Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?
Data augmentation via back-translation is common when pretraining Vision-and-Language Navigation (VLN) models, even though the generated instructions are noisy.
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information.
Pre-trained Language Models as Prior Knowledge for Playing Text-based Games
Given the sample-inefficiency of RL approaches, it is inefficient to learn rich enough textual representations to be able to understand and reason using the textual observation in such a complicated game environment setting.
Adapting a Language Model for Controlled Affective Text Generation
The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text.
Differentially-private Federated Neural Architecture Search
To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties.
