2 citations · 4 across the 7 of their papers we have counts for
8 papers
SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
Yuxuan Kuang, Haoran Geng, Amine Elhafsi +5
Humanoid robots hold significant potential in accomplishing daily tasks across diverse environments thanks to their flexibility and human-like morphology. Recent works have made si…
Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning
Amine Elhafsi, Daniel Morton, Marco Pavone
Autonomous robots must reason about the physical consequences of their actions to operate effectively in unstructured, real-world environments. We present Scan, Materialize, Simula…
Vision Foundation Model Embedding-Based Semantic Anomaly Detection
Max Peter Ronecker, Matthew Foutter, Amine Elhafsi +4
Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonom…
Real-Time Anomaly Detection and Reactive Planning with Large Language Models
Rohan Sinha, Amine Elhafsi, Christopher Agia +3
Foundation models, e.g., large language models (LLMs), trained on internet-scale data possess zero-shot generalization capabilities that make them a promising technology towards de…
Semantic Anomaly Detection with Large Language Models
Amine Elhafsi, Rohan Sinha, Christopher Agia +3
As robots acquire increasingly sophisticated skills and see increasingly complex and varied environments, the threat of an edge case or anomalous failure is ever present. For examp…
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
Boris Ivanovic, Amine Elhafsi, Guy Rosman +2
Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to i…
Map-Predictive Motion Planning in Unknown Environments
Amine Elhafsi, Boris Ivanovic, Lucas Janson +1
Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods…
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Sandeep Chinchali, Apoorva Sharma, James Harrison +6
Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and obje…