7 citations · 13 across the 19 of their papers we have counts for
19 papers
Topology-Guided ORCA: Smooth Multi-Agent Motion Planning in Constrained Environments
Fatemeh Cheraghi Pouria, Zhe Huang, Ananya Yammanuru +2
We present Topology-Guided ORCA as an alternative simulator to replace ORCA for planning smooth multi-agent motions in environments with static obstacles. Despite the impressive pe…
LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
Zhe Huang, John Pohovey, Ananya Yammanuru +1
Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions to achieve tasks in an open world. However, when…
A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping
Abhi Kamboj, Katherine Driggs-Campbell
Robotic grasping presents a difficult motor task in real-world scenarios, constituting a major hurdle to the deployment of capable robots across various industries. Notably, the sc…
W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics
Andre Schreiber, Arun N. Sivakumar, Peter Du +3
Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with…
Towards Provable Log Density Policy Gradient
Pulkit Katdare, Anant Joshi, Katherine Driggs-Campbell
Policy gradient methods are a vital ingredient behind the success of modern reinforcement learning. Modern policy gradient methods, although successful, introduce a residual error…
Marginalized Importance Sampling for Off-Environment Policy Evaluation
Pulkit Katdare, Nan Jiang, Katherine Driggs-Campbell
Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in si…