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arxiv: 2604.17833 · v2 · submitted 2026-04-20 · 💻 cs.RO

DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation

Autrio Das , Shreya Bollimuntha , Madala Venkata Renu Jeevesh , Keshab Patra , Tashmoy Ghosh , Nagamanikandan Govindan , Arun Kumar Singh , K Madhava Krishna This is my paper

Pith reviewed 2026-05-10 05:00 UTC · model grok-4.3

classification 💻 cs.RO
keywords dual-arm manipulationnon-prehensile manipulationmodel predictive controlimpedance controlreinforcement learningdynamics modelingservice robotics
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The pith

DART integrates nonlinear MPC with an impedance controller and three tray-object dynamics models to achieve precise non-prehensile dual-arm manipulation on a moving tray.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces DART to let two robot arms transport and reposition objects on a tray without grasping them, a task that expands service-robot capabilities in settings like hotels. It embeds nonlinear model predictive control inside an optimization-based impedance controller so the object stays in accurate motion relative to the tray. The central evaluation compares three ways to predict how the tray and object interact inside the MPC: a fixed physics model, a model that fits fresh data online, and a reinforcement-learning model trained to handle varied object properties. Simulation tests across objects of different mass, shape, and friction show measurable differences in settling time, steady-state error, control effort, and generalization. The authors position the work as the first complete framework for this class of dual-arm non-prehensile tray tasks.

Core claim

DART achieves accurate object motion relative to a dynamically controlled tray by coupling nonlinear model predictive control with an optimization-based impedance controller. The MPC uses one of three complementary tray-object dynamics models as its state-transition function: a physics-based analytical model, an online regression-based identification model that adapts in real time, or a reinforcement-learning-based model that generalizes across object properties. Validation in simulation with objects of varying mass, geometry, and friction coefficients reveals the performance trade-offs among the three modeling choices.

What carries the argument

Nonlinear MPC whose state-transition function is supplied by one of three tray-object dynamics models (physics analytical, online regression, or RL), paired with an optimization-based impedance controller that enforces relative motion accuracy.

If this is right

  • Accurate relative motion between object and tray is maintained in simulation for objects differing in mass, geometry, and friction.
  • Clear performance trade-offs appear among the three modeling strategies on settling time, steady-state error, control effort, and cross-object generalization.
  • The resulting controller supports precise non-prehensile transport tasks relevant to hospitality and service robotics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If sim-to-real transfer succeeds, the same MPC structure could be reused for other sliding or pushing tasks that do not require grasping.
  • The RL dynamics model could let the system handle previously unseen object properties without redesigning the controller.
  • Extending the impedance layer to multiple trays or moving bases would test whether the relative-motion formulation scales beyond a single tray.

Load-bearing premise

The three dynamics models inside the MPC will transfer from simulation to real hardware with comparable settling time, error, and generalization.

What would settle it

A hardware experiment on the same tray and objects that shows one or more of the three dynamics models producing settling times or steady-state errors at least twice as large as the simulation results, or complete loss of generalization to a new object.

Figures

Figures reproduced from arXiv: 2604.17833 by Arun Kumar Singh, Autrio Das, Keshab Patra, K Madhava Krishna, Madala Venkata Renu Jeevesh, Nagamanikandan Govindan, Shreya Bollimuntha, Tashmoy Ghosh.

Figure 1
Figure 1. Figure 1: Non-Prehensile Manipulation using a Dual xArm7 setup. Our framework transports a bowl of fruits to the goal location on the tray through a sequence of tilts given by MPC executed by the dual-arm system. affects object trajectories while maintaining stable, com￾pliant control of the two manipulators. When two robotic arms coordinate to manipulate a tray for controlling object motion on its surface, several … view at source ↗
Figure 2
Figure 2. Figure 2: Task Setup: Two robotic arms are placed with the bases fixed to the ground. The tray is rigidly grasped and an object is placed on the tray. The world frame is represented by {W}, body frame by {B} and object frame by {O}. A. Object–Tray Model The pose of the object in {W} is defined as x = [p ⊤, θ ⊤] ⊤, where p ∈ R 3 is the position and θ ∈ R 3 is the orientation (roll-pitch-yaw). The linear and angular v… view at source ↗
Figure 3
Figure 3. Figure 3: DART Framework: Our proposed framework takes the current object state X and the desired target state Xref. We choose ν ref as 06×1 as inputs. These are fed into a nonlinear MPC, which computes the optimal tray-tilt commands (u). These commands are then passed to an optimization-based impedance controller, which computes the torques required to realize the tilts. Feedback from the simulator updates the obje… view at source ↗
Figure 4
Figure 4. Figure 4: Performance of two objects, Cube on the left and Cylinder [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualizations of the trajectories: This figure de￾picts the trajectories followed by a cube(green), sphere(red), and a cylinder(purple) with mass 1kg and friction 0.2 , manipulated by using 3 different MPC methods. The curves shows only the path of the object taken on the tray, longer path doesn’t mean longer convergence. The color legend corresponds to the three methods as shown on top. genaralization ov… view at source ↗
read the original 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 reinforcement learning-based dynamics model that generalizes across object properties. Our pipeline is validated in simulation with objects of varying mass, geometry, and friction coefficients. Extensive evaluations highlight the trade-offs among the three modeling strategies in terms of settling time, steady-state error, control effort, and generalization across objects. To the best of our knowledge, DART constitutes the first framework for non-prehensile dual-arm manipulation of objects on a tray. Project Link: https://dart-icra.github.io/dart/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims to introduce the first framework, DART, for non-prehensile dual-arm manipulation of objects on a tray using nonlinear MPC combined with impedance control. It evaluates three complementary dynamics modeling strategies (physics-based analytical, online regression identification, and RL-based) as the state transition function in MPC, and validates them in simulation across objects with varying mass, geometry, and friction, highlighting trade-offs in settling time, steady-state error, control effort, and generalization.

Significance. If the simulation-based results on the three modeling strategies hold and transfer to hardware, the work would be significant for service robotics by providing a systematic comparison of dynamics models for MPC in dual-arm non-prehensile tasks. The RL-based model for generalization is particularly promising. However, the current lack of real-world validation limits the assessed impact.

major comments (2)
  1. [Abstract] The central performance claims (trade-offs in settling time, steady-state error, control effort, and generalization) rest entirely on simulation results, as stated in the abstract. No real-robot experiments, hardware description, or analysis of sim-to-real gaps (e.g., friction stochasticity, vision latency) are mentioned, despite the practical relevance emphasized. This is load-bearing for the generalization and accuracy claims.
  2. [Evaluation] The manuscript validates the pipeline 'in simulation with objects of varying mass, geometry, and friction coefficients' but the abstract provides no quantitative metrics, error bars, or statistical analysis, and the evaluation appears to lack ablation studies isolating the contribution of the impedance controller or tests for statistical significance of the reported trade-offs.
minor comments (1)
  1. The term 'nonprehensilely' in the abstract should be hyphenated as 'non-prehensilely' for standard terminology.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive and detailed review. We appreciate the recognition of the potential significance of comparing dynamics modeling strategies for dual-arm non-prehensile manipulation. Below we provide point-by-point responses to the major comments and describe the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] The central performance claims (trade-offs in settling time, steady-state error, control effort, and generalization) rest entirely on simulation results, as stated in the abstract. No real-robot experiments, hardware description, or analysis of sim-to-real gaps (e.g., friction stochasticity, vision latency) are mentioned, despite the practical relevance emphasized. This is load-bearing for the generalization and accuracy claims.

    Authors: We agree that all reported results are obtained in simulation, as explicitly stated throughout the manuscript, and that this constrains the strength of claims about real-world generalization and accuracy. The simulation environment enables systematic variation of object mass, geometry, and friction that would be difficult to control on hardware. To address the concern, we will revise the abstract to include key quantitative metrics from the simulations and add a dedicated limitations section that discusses sim-to-real gaps, including friction stochasticity, vision latency, and other unmodeled effects, along with planned future hardware validation. We cannot, however, add real-robot experiments within the scope of this revision. revision: partial

  2. Referee: [Evaluation] The manuscript validates the pipeline 'in simulation with objects of varying mass, geometry, and friction coefficients' but the abstract provides no quantitative metrics, error bars, or statistical analysis, and the evaluation appears to lack ablation studies isolating the contribution of the impedance controller or tests for statistical significance of the reported trade-offs.

    Authors: We thank the referee for this observation. The current evaluation section reports results across multiple objects and scenarios but does not include error bars, formal statistical tests, or explicit ablations isolating the impedance controller. We will update the abstract to report quantitative metrics with variability measures. In the revised evaluation, we will add error bars and standard deviations to all plots, include ablation studies that disable the impedance controller to quantify its contribution, and perform statistical significance tests (e.g., paired t-tests) on the observed differences in settling time, steady-state error, and control effort. These changes will be incorporated in the next version. revision: yes

standing simulated objections not resolved
  • Real-robot experiments and hardware validation, which are outside the current simulation-focused scope of the manuscript and cannot be added without new physical experiments.

Circularity Check

0 steps flagged

No circularity in derivation chain; three dynamics models are independent alternatives

full rationale

The manuscript presents DART as a framework integrating three distinct dynamics modeling strategies (physics-based analytical, online regression identification, RL-based) as alternatives inside nonlinear MPC plus impedance control. These are described as complementary and comparatively evaluated on simulation metrics without any reduction of one to another by construction, fitted-parameter renaming, or self-citation chains. No equations or claims equate a prediction to its own inputs; the novelty claim is a standard 'first framework' statement unsupported by load-bearing self-citations. The derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; full manuscript would be required to identify any fitted scales, domain assumptions about tray-object contact, or new control primitives.

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