29 citations · 76 across the 7 of their papers we have counts for
6 papers · 1 filter
Learning to Retrieve Relevant Experiences for Motion Planning
Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava +1
Recent work has demonstrated that motion planners' performance can be significantly improved by retrieving past experiences from a database. Typically, the experience database is q…
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Constantinos Chamzas, Martina Lippi, Michael C. Welle +3
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the rec…
KDF: Kinodynamic Motion Planning via Geometric Sampling-based Algorithms and Funnel Control
Christos K. Verginis, Dimos V. Dimarogonas, Lydia E. Kavraki
We integrate sampling-based planning techniques with funnel-based feedback control to develop KDF, a new framework for solving the kinodynamic motion-planning problem via funnel co…
Path Planning for Manipulation using Experience-driven Random Trees
Èric Pairet, Constantinos Chamzas, Yvan Petillot +1
Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferab…
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
W. Cannon Lewis, Mark Moll, Lydia E. Kavraki
Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodele…
Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments
Muhayyuddin, Mark Moll, Lydia Kavraki +1
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstruct…