5 citations · 8 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
PLATO: Planning with LLMs and Affordances for Tool Manipulation
Arvind Car, Sai Sravan Yarlagadda, Alison Bartsch +2
As robotic systems become increasingly integrated into complex real-world environments, there is a growing need for approaches that enable robots to understand and act upon natural…
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…
BeadSight: An Inexpensive Tactile Sensor Using Hydro-Gel Beads
Abraham George, Yibo Chen, Atharva Dikshit +2
In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sig…
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…
One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers
Abraham George, Amir Barati Farimani
Learning from human demonstrations (behavior cloning) is a cornerstone of robot learning. However, most behavior cloning algorithms require a large number of demonstrations to lear…
Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction
Jong Hoon Park, Gauri Pramod Dalwankar, Alison Bartsch +2
Accurately determining fluid viscosity is crucial for various industrial and scientific applications. Traditional methods of viscosity measurement, though reliable, often require m…
RoboChop: Autonomous Framework for Fruit and Vegetable Chopping Leveraging Foundational Models
Atharva Dikshit, Alison Bartsch, Abraham George +1
With the goal of developing fully autonomous cooking robots, developing robust systems that can chop a wide variety of objects is important. Existing approaches focus primarily on…
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning
Abraham George, Alison Bartsch, Amir Barati Farimani
The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance. However, any requirement for a human to manually 'teach' the model…