🤖 Humanoid 🦾 Industrial & Cobot 🚚 AGV / AMR 🐕 Quadruped ⚙️ Reducers · Servos · Sensors 🚁 Drones & Autonomy 🧠 Embodied AI
Robos News
Robotics

MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation

arXiv:2504.16738v3 Announce Type: replace Abstract: Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills. Solutions to this problem lie in an infinitely vast space of parameterized skill sequences -- a space where common incremental methods struggle to find sequences that have non-obvious intermediate steps. Some approac

Published July 7, 2026 · Category: Robotics

Overview

arXiv:2504.16738v3 Announce Type: replace Abstract: Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills. Solutions to this problem lie in an infinitely vast space of parameterized skill sequences -- a space where common incremental methods struggle to find sequences that have non-obvious intermediate steps. Some approaches reason over lower-dimensional, symbolic spaces, which are more tractable to explore but may be brittle and are laborious to construct. In this work, we introduce MOSAIC, a skill-centric, multi-directional planning approach that targets these challenges by reasoning about which skills to employ and where they are most likely to succeed, by utilizing physics simulation to estimate skill execution outcomes. Specifically, MOSAIC employs two complementary skill families: Generators, which identify ``islands of competence'' where skills are demonstrably effective, and Connectors, which link these skill-trajectories by solving boundary value problems. By focusing planning efforts on regions of high competence, MOSAIC efficiently discovers physically-grounded solutions. We demonstrate its efficacy on complex long-horizon problems in both simulation and the real world, using a diverse set of skills including generative diffusion models, motion planning algorithms, and manipulation-specific models. Visit skill-mosaic.github.io for demonstrations and examples.

Source

Originally published at arxiv.org.

Related Articles

CD
Robos News Newsroom

Robos News covers markets, crypto and commodities for Asia & the Middle East — tier-1 desk research, AI-driven analysis, institutional-grade data. Tip our newsroom: [email protected]

Email the newsroom →
Disclaimer: This article is for informational purposes only and does not constitute investment advice. Data may be delayed up to 15 minutes. Past performance is not indicative of future results. Consult a licensed financial advisor before making investment decisions.

Related Stories

More from News →