29 citations · 30 across the 3 of their papers we have counts for
3 papers
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…
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…