TL;DR
DART introduces a dual-arm manipulation framework combining nonlinear MPC with various object dynamics models, enabling precise non-prehensile object handling on a tray in simulation, with a focus on model trade-offs.
Contribution
It is the first framework for non-prehensile dual-arm manipulation of objects on a tray, integrating multiple dynamics modeling strategies within MPC.
Findings
Reinforcement learning model generalizes across object properties.
Physics-based model offers fast response with less accuracy.
Online regression adapts to changing object dynamics.
Abstract
What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult, and the complexity is amplified in dual-arm settings. Such tasks are highly relevant to service robotics in domains such as hotels and hospitality, where robots must transport and reposition diverse objects with precision. We present DART, a novel dual-arm framework that integrates nonlinear Model Predictive Control (MPC) with an optimization-based impedance controller to achieve accurate object motion relative to a dynamically controlled tray. The framework systematically evaluates three complementary strategies for modeling tray-object dynamics as the state transition function within our MPC formulation: (i) a physics-based analytical model, (ii) an online regression based identification model that adapts in real-time, and (iii) a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
