arXiv subjects

Nicolas Marticorena

Publications and source records attributed to Nicolas Marticorena.

3 recordsLinked to original sources

arXiv2026

ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat

Reactive control can gracefully coordinate the motion of the base and the arm of a mobile manipulator. However, incorporating an accurate representation of the environment to avoid obstacles without involving costly planning remains a challenge. In this work, we present ReMoSPLAT, a reactive controller based on a quadratic program formulation for mobile manipulation that leverages a Gaussian Splat representation for collision avoidance. By integrating additional constraints and costs into the optimisation formulation, a mobile manipulator platform can reach its intended end effector pose while avoiding obstacles, even in cluttered scenes. We investigate the trade-offs of two methods for efficiently calculating robot-obstacle distances, comparing a purely geometric approach with a rasterisation-based approach. Our simulation experiments on both synthetic and real-world scans demonstrate the feasibility of the proposed method, achieving performance comparable to controllers that rely on perfect ground-truth information. We further validate the approach on a real robot platform more details: https://remosplat.github.io

cs.RO↗

arXiv2025

Open-Vocabulary Part-Based Grasping

Many robotic tasks require grasping objects at specific object parts instead of arbitrarily, a crucial capability for interactions beyond simple pick-and-place, such as human-robot interaction, handovers, or tool use. Prior work has focused either on generic grasp prediction or task-conditioned grasping, but not on directly targeting object parts in an open-vocabulary way. We propose AnyPart, a modular framework that unifies open-vocabulary object detection, part segmentation, and 6-DoF grasp prediction to enable robots to grasp user-specified parts of arbitrary objects based on natural language prompts. We evaluate 16 model combinations, and demonstrate that the best-performing combination achieves 60.8% grasp success in cluttered real-world scenes at 60 times faster inference than existing approaches. To support this study, we introduce a new dataset for part-based grasping and conduct a detailed failure analysis. Our core insight is that modularly combining existing foundation models unlocks surprisingly strong and efficient capabilities for open-vocabulary part-based grasping without requiring additional training.

cs.RO↗

arXiv2025

RMMI: Reactive Mobile Manipulation using an Implicit Neural Map

Mobile manipulator robots operating in complex domestic and industrial environments must effectively coordinate their base and arm motions while avoiding obstacles. While current reactive control methods gracefully achieve this coordination, they rely on simplified and idealised geometric representations of the environment to avoid collisions. This limits their performance in cluttered environments. To address this problem, we introduce RMMI, a reactive control framework that leverages the ability of neural Signed Distance Fields (SDFs) to provide a continuous and differentiable representation of the environment's geometry. RMMI formulates a quadratic program that optimises jointly for robot base and arm motion, maximises the manipulability, and avoids collisions through a set of inequality constraints. These constraints are constructed by querying the SDF for the distance and direction to the closest obstacle for a large number of sampling points on the robot. We evaluate RMMI both in simulation and in a set of real-world experiments. For reaching in cluttered environments, we observe a 25% increase in success rate. For additional details, code, and experiment videos, please visit https://rmmi.github.io/.

cs.RO↗