8 papers · 1 filter
LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs
Abraham George, Amir Barati Farimani
We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of human demonstrations (as few as…
RT-Cache: Training-Free Retrieval for Real-Time Manipulation
Owen Kwon, Abraham George, Alison Bartsch +1
Real robots are expected to repeat the same behavior in new environments with very little new data, yet modern controllers either incur heavy per-step inference or require deployme…
Semantic Intelligence: Integrating GPT-4 with A Planning in Low-Cost Robotics
Jesse Barkley, Abraham George, Amir Barati Farimani
Classical robot navigation often relies on hardcoded state machines and purely geometric path planners, limiting a robot's ability to interpret high-level semantic instructions. In…
LLM-Drone: Aerial Additive Manufacturing with Drones Planned Using Large Language Models
Akshay Raman, Chad Merrill, Abraham George +1
Additive manufacturing (AM) has transformed the production landscape by enabling the precision creation of complex geometries. However, AM faces limitations when applied to challen…
Low Fidelity Visuo-Tactile Pretraining Improves Vision-Only Manipulation Performance
Selam Gano, Abraham George, Amir Barati Farimani
Tactile perception is essential for real-world manipulation tasks, yet the high cost and fragility of tactile sensors can limit their practicality. In this work, we explore BeadSig…
VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies
Abraham George, Selam Gano, Pranav Katragadda +1
Tactile information is a critical tool for dexterous manipulation. As humans, we rely heavily on tactile information to understand objects in our environments and how to interact w…