5 papers
Learning Task and Motion Plans from Real Demonstrations with Hybrid Flow Matching
Zuleika Redondo Garcia, Andreu Matoses Gimenez, Javier Alonso-Mora
Long-horizon mobile manipulation requires a task plan and the motion that executes it. Generative planners trained on demonstration produce both in one pass, requiring neither a sy…
Flow Policies as Actions of Skill-Level World Models: Learned and Symbolic Abstractions for Long-Horizon Planning
Andreu Matoses Gimenez, Andrei-Carlo Papuc, Chris Pek +1
Latent world models enable robots to plan by predicting the consequences of actions. Planning long tasks with control-rate actions requires many prediction steps, which enlarges th…
Cross-Entropy Optimization of Physically Grounded Task and Motion Plans
Andreu Matoses Gimenez, Nils Wilde, Chris Pek +1
Autonomously performing tasks often requires robots to plan high-level discrete actions and continuous low-level motions to realize them. Previous TAMP algorithms have focused main…
SADCHER: Scheduling using Attention-based Dynamic Coalitions of Heterogeneous Robots in Real-Time
Jakob Bichler, Andreu Matoses Gimenez, Javier Alonso-Mora
We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher…
Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning
Jack Zeng, Andreu Matoses Gimenez, Eugene Vinitsky +2
This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method levera…