6 papers · 1 filter
Training and Evaluating Diffusion Policies with Long Context Lengths
Abhinav Agarwal, Adam Wei, Taylan Kargin +6
Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only…
Semidefinite Relaxations for Collision-Free Motion Planning
Bernhard Paus Graesdal, Alexandre Amice, Pablo A. Parrilo +1
We study semidefinite relaxations for collision-free motion planning. We focus on a point robot moving from start to goal through spherical obstacles in , subject to…
Mixed Discrete and Continuous Planning using Shortest Walks in Graphs of Convex Sets
Savva Morozov, Tobia Marcucci, Bernhard Paus Graesdal +3
We study the Shortest-Walk Problem (SWP) in a Graph of Convex Sets (GCS). A GCS is a graph where each vertex is paired with a convex program, and each edge couples adjacent program…
A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling
Lujie Yang, Tobia Marcucci, Pablo A. Parrilo +1
We introduce a semidefinite relaxation for optimal control of linear systems with time scaling. These problems are inherently nonconvex, since the system dynamics involves bilinear…
Multi-Query Shortest-Path Problem in Graphs of Convex Sets
Savva Morozov, Tobia Marcucci, Alexandre Amice +4
The Shortest-Path Problem in Graph of Convex Sets (SPP in GCS) is a recently developed optimization framework that blends discrete and continuous decision making. Many relevant pro…
Towards Tight Convex Relaxations for Contact-Rich Manipulation
Bernhard Paus Graesdal, Shao Yuan Chew Chia, Tobia Marcucci +4
We present a novel method for global motion planning of robotic systems that interact with the environment through contacts. Our method directly handles the hybrid nature of such t…