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Joaquim Ortiz-Haro

15 accepted papers

2026

Infinite-Horizon Value Function Approximation for Model Predictive Control

Armand Jordana, Sebastien Kleff, Arthur Haffemayer, Joaquim Ortiz-Haro, Justin Carpentier, Nicolas Mansard +1
ICRA 2026poster

Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully desig…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

WorldPlanner: Monte Carlo Tree Search and MPC with Action-Conditioned Visual World Models

Rooholla Khorrambakht, Joaquim Ortiz-Haro, Joseph Amigo, Omar Mostafa, Daniel Dugas, Franziska Meier +1
ICRA 2026poster

Robots must understand their environment from raw sensory inputs and reason about the consequences of their actions in it to solve complex tasks. Behavior Cloning (BC) leverages task-specific human demonstrations to learn this knowledge as end-to-end policies. However, these policies are difficult t…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

db-ECBS: Interaction-Aware Multirobot Kinodynamic Motion Planning (Abstract Reprint)

Akmaral Moldagalieva, Joaquim Ortiz-Haro, Wolfgang Hönig
AAAI 2026technical

Kinodynamic motion planning for a multirobot system with different dynamics and actuation limits is a challenging problem. The difficulty increases with the presence of aerodynamic interaction forces that occur when aerial robots fly in close proximity. Due to these complexities, existing planners e

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Effort Level Search in Infinite Completion Trees with Application to Task-and-Motion Planning

Marc Toussaint, Joaquim Ortiz-Haro, Valentin N. Hartmann, Erez Karpas, Wolfgang Hönig
ICRA 2024poster

Solving a Task-and-Motion Planning (TAMP) problem can be represented as a sequential (meta-) decision process, where early decisions concern the skeleton (sequence of logic actions) and later decisions concern what to compute for such skeletons (e.g., action parameters, bounds, RRT paths, or full op…

Cited by 1Source ↗Scholar ↗
2024

Kinodynamic Motion Planning for a Team of Multirotors Transporting a Cable-Suspended Payload in Cluttered Environments

Khaled Wahba, Joaquim Ortiz-Haro, Marc Toussaint, Wolfgang Hönig
IROS 2024poster

We propose a motion planner for cable-driven payload transportation using multiple unmanned aerial vehicles (UAVs) in an environment cluttered with obstacles. Our planner is kinodynamic, i.e., it considers the full dynamics model of the transporting system including actuation constraints. Due to the…

Cited by 4Source ↗Scholar ↗
2024

Solving Sequential Manipulation Puzzles by Finding Easier Subproblems

Svetlana Levit, Joaquim Ortiz-Haro, Marc Toussaint
ICRA 2024poster

We consider a set of challenging sequential manipulation puzzles, where an agent has to interact with multiple movable objects and navigate narrow passages. Such settings are notoriously difficult for Task-and-Motion Planners, as they require interdependent regrasps and solving hard motion planning…

Cited by 4Source ↗code ↗Scholar ↗
2024

db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning

Akmaral Moldagalieva, Joaquim Ortiz-Haro, Marc Toussaint, Wolfgang Hönig
ICRA 2024poster

This paper presents a multi-robot kinodynamic motion planner that enables a team of robots with different dynamics, actuation limits, and shapes to reach their goals in challenging environments. We solve this problem by combining Conflict-Based Search (CBS), a multi-agent path finding method, and di…

Cited by 13Source ↗code ↗Scholar ↗
2024

iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization

Joaquim Ortiz-Haro, Wolfgang Hönig, Valentin N. Hartmann, Marc Toussaint, Ludovic Righetti
IROS 2024poster

Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it requires solving two-value boundary problems (com…

Cited by 5Source ↗Scholar ↗
2023

Efficient Path Planning In Manipulation Planning Problems by Actively Reusing Validation Effort

Valentin N. Hartmann, Joaquim Ortiz-Haro, Marc Toussaint
IROS 2023poster

The path planning problems arising in manipulation planning and in task and motion planning settings are typically repetitive: the same manipulator moves in a space that only changes slightly. Despite this potential for reuse of information, few planners fully exploit the available information. To b…

Cited by 2Source ↗Scholar ↗
2023

Learning Feasibility of Factored Nonlinear Programs in Robotic Manipulation Planning

Joaquim Ortiz-Haro, Jung-Su Ha, Danny Driess, Erez Karpas, Marc Toussaint
ICRA 2023poster

A factored Nonlinear Program (Factored-NLP) explicitly models the dependencies between a set of continuous variables and nonlinear constraints, providing an expressive formulation for relevant robotics problems such as manipulation planning or simultaneous localization and mapping. When the problem…

Cited by 3Source ↗Scholar ↗
2022

BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning

Jay Kamat, Joaquim Ortiz-Haro, Marc Toussaint, Florian T. Pokorny, Andreas Orthey
IROS 2022poster

Optimal sampling based motion planning and trajectory optimization are two competing frameworks to generate optimal motion plans. Both frameworks have complementary properties: Sampling based planners are typically slow to converge, but provide optimality guarantees. Trajectory optimizers, however,…

Cited by 17Source ↗code ↗Scholar ↗
2022

RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning

Cornelius V. Braun, Joaquim Ortiz-Haro, Marc Toussaint, Ozgur S. Oguz
IROS 2022poster

Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we pr…

Cited by 18Source ↗code ↗Scholar ↗
2022

db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning

Wolfgang Hönig, Joaquim Ortiz-Haro, Marc Toussaint
IROS 2022poster

We consider time-optimal motion planning for dynamical systems that are translation-invariant, a property that holds for many mobile robots, such as differential-drives, cars, airplanes, and multirotors. Our key insight is that we can extend graph-search algorithms to the continuous case when used s…

Cited by 14Source ↗code ↗Scholar ↗
2021

Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation

Joaquim Ortiz-Haro, Valentin N. Hartmann, Ozgur S. Oguz, Marc Toussaint
ICRA 2021poster

Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality…

Cited by 17Source ↗Scholar ↗
2021

Structured deep generative models for sampling on constraint manifolds in sequential manipulation

Joaquim Ortiz-Haro, Jung-Su Ha, Danny Driess, Marc Toussaint
CoRL 2021poster

Sampling efficiently on constraint manifolds is a core problem in robotics. We propose Deep Generative Constraint Sampling (DGCS), which combines a deep generative model for sampling close to a constraint manifold with nonlinear constrained optimization to project to the constraint manifold. The gen…

Cited by 30Source ↗Scholar ↗