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Found 6 papers, 1 papers with code
BeSimulator: A Large Language Model Powered Text-based Behavior Simulator
Traditional robot simulators focus on physical process modeling and realistic rendering, often suffering from high computational costs, inefficiencies, and limited adaptability.
Task2Morph: Differentiable Task-inspired Framework for Contact-Aware Robot Design
Optimizing the morphologies and the controllers that adapt to various tasks is a critical issue in the field of robot design, aka.
A Study on Training and Developing Large Language Models for Behavior Tree Generation
The core contribution of this paper lies in the design of a BT generation framework based on LLM, which encompasses the entire process, from data synthesis and model training to application developing and data verification.
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification
In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen.
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device- and topology- heterogeneous ML clusters.
OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model.
