6 papers
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…
Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge
Jesse Barkley, Abraham George, Amir Barati Farimani
Deploying autonomous edge robotics in dynamic military environments is constrained by both scarce domain-specific training data and the computational limits of edge hardware. This…
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…