activity
20172022
most citedPath Planning for Manipulation using Experience-driven Random Trees

29 citations · 76 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI20221 cited

Synthesis from Satisficing and Temporal Goals

Suguman Bansal, Lydia Kavraki, Moshe Y. Vardi +1

Reactive synthesis from high-level specifications that combine hard constraints expressed in Linear Temporal Logic LTL with soft constraints expressed by discounted-sum (DS) reward…

cs.RO20221 cited

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…

cs.RO2021

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…

cs.RO2021

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…

cs.RO202129 cited

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…

cs.LO202018 cited

LTLf Synthesis on Probabilistic Systems

Andrew M. Wells, Morteza Lahijanian, Lydia E. Kavraki +1

Many systems are naturally modeled as Markov Decision Processes (MDPs), combining probabilities and strategic actions. Given a model of a system as an MDP and some logical specific…