activity
20202024
most citedStochastic Finite State Control of POMDPs with LTL Specifications

12 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2024

A Safety-Critical Framework for UGVs in Complex Environments: A Data-Driven Discrepancy-Aware Approach

Skylar X. Wei, Lu Gan, Joel W. Burdick

This work presents a novel data-driven multi-layered planning and control framework for the safe navigation of a class of unmanned ground vehicles (UGVs) in the presence of unknown…

cs.RO2023

The Fractal Hand-II: Reviving a Classic Mechanism for Contemporary Grasping Challenges

Malcolm G. A. Tisdale, Joel W. Burdick

This paper, and its companion, propose a new fractal robotic gripper, drawing inspiration from the century-old Fractal Vise. The unusual synergistic properties allow it to passivel…

cs.RO20233 cited

An Active Learning Based Robot Kinematic Calibration Framework Using Gaussian Processes

Ersin Daş, Joel W. Burdick

Future NASA lander missions to icy moons will require completely automated, accurate, and data efficient calibration methods for the robot manipulator arms that sample icy terrains…

cs.RO2023

Adaptive Coverage Path Planning for Efficient Exploration of Unknown Environments

Amanda Bouman, Joshua Ott, Sung-Kyun Kim +5

We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked w…

cs.AI202012 cited

Stochastic Finite State Control of POMDPs with LTL Specifications

Mohamadreza Ahmadi, Rangoli Sharan, Joel W. Burdick

Partially observable Markov decision processes (POMDPs) provide a modeling framework for autonomous decision making under uncertainty and imperfect sensing, e.g. robot manipulation…