Intent-Informed Model Predictive Control for Shared Autonomy in Remote Manipulation Tasks

Lorena Maria Genua; Camilla Tabasso; Mya Schroder; Joshua Migdal; Tye Brady; Taskin Padir

cs.ROOct 18, 2026

Intent-Informed Model Predictive Control for Shared Autonomy in Remote Manipulation Tasks

Authors: Lorena Maria Genua, Camilla Tabasso, Mya Schroder, Joshua Migdal, Tye Brady, Taskin Padir

Organizations: Amazon Fulfillment Technologies & Robotics, Westborough MA, USA · Institute for Experiential Robotics, Northeastern University, Boston MA, USA · School of Computer, Data & Information Sciences, University of Wisconsin - Madison, Madison WI, USA

Abstract

Shared autonomy is a control paradigm that combines human cognitive abilities with robot precision to enable effective remote manipulation in complex environments. A key challenge is how to adapt the level of autonomy using information extracted from the human, task, and/or environment. In this paper, we propose a novel arbitration strategy that dynamically adjusts the level of autonomy in the system using the entropy of a belief distribution derived from a multimodal intent inference. Human intent is modeled with a Dynamic Bayesian Network (DBN), which updates beliefs over goals given real-time operator gaze data and robot arm motion during teleoperation. We then formulate the blending of human input and autonomous control as a Model Predictive Control (MPC) problem, where the system's autonomous behavior is guided by the belief distribution. This approach enables continuous, gradual transitions across the full spectrum of autonomy levels, from direct teleoperation to full autonomy. To validate our approach, we conducted experiments to compare the performance of our proposed algorithm against two MPC-based shared autonomy methods. Compared to the baselines, our approach achieved reductions of at least 61% in object retrieval time, 44% in trajectory length, and 60% in human control input. Participants reported high perceived control, predictability, intent recognition, and responsiveness, along with low workload.

Figures & tables

Figure 1: Intent-Informed Model Predictive Control uses the entropy of the belief distribution derived from human intent inference to determine the control authority between the human operator and the system.

Figure 2: Methodology overview. Our approach combines intent inference using a Dynamic Bayesian Network (DBN) with Model Predictive Control (MPC) for adaptive shared autonomy. The DBN continuously updates beliefs on the goals based on operator gaze and robot motion. The MPC formulation uses the entropy of these beliefs to adjust the level of autonomy, ranging from direct teleoperation when entropy is high to full autonomy when entropy is low.

Parameter	Value	Parameter	Value
$\alpha$	0.2	$P$	$30I$
$T$	10	$Q$	$30I$
$n_{g}$	$6$	$w_{rot}$	$5$

Table I: System Parameters

Figure 3: Experimental setup. a) The operator uses an Xbox controller to teleoperate the robot through a screen-based user interface. The eye tracker is mounted on the monitor. b) Robotic system. For the experiment, we use the left arm with the FinRay gripper. The right arm is not operational.

Figure 4: Experimental results. Our proposed method Intent-Informed MPC resulted in a) faster object retrieval time, b) lower trajectory length and c) lower human control input. Statistical significance ( $p<0.001$ ) is denoted by ***.

Figure 5: Time plot of entropy and target belief in a representative trial. As entropy decreases, the system increases the level of autonomy.

Question	Intent-MPC (Ours)		A-MPC		B-MPC		Intent vs A	Intent vs B	B vs A
Question	Mean	SD	Mean	SD	Mean	SD	$p$ -value	$p$ -value	$p$ -value
Q1 Control	5.65	1.27	3.10	1.37	5.80	1.01	$<0.01$	$>0.05$	$<0.001$
Q2 Predictability	6.35	0.75	2.80	1.28	6.35	0.67	$<0.001$	$>0.05$	$<0.001$
Q3 Intent Understanding	6.35	0.99	3.00	1.34	6.10	1.21	$<0.001$	$>0.05$	$<0.01$
Q4 Responsiveness	6.35	0.75	3.40	1.10	6.30	0.80	$<0.01$	$>0.05$	$<0.01$
Q5 Mental Demand	1.95	0.94	4.15	1.27	2.85	1.31	$<0.01$	$>0.05$	$>0.05$

Table II: Mean and standard deviation (7-point Likert scale) for each subjective question across control modes. Pairwise comparisons report Bonferroni-corrected

p

-values with significant differences (

p<0.05

) shown in bold.

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Robotic manipulation studies how robots perceive, plan, and control physical interactions to grasp, move, assemble, and use objects.

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Nov 12, 2025J/K move · Enter open · S save

A Shared Control Framework for Mobile Robots with Planning-Level Intention Prediction

Jinyu Zhang, Lijun Han, Feng Jian, Lingxi Zhang+1

Mobile Robotics Human-Robot Collaboration Robot Motion Planning

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In mobile robot shared control, effectively understanding human motion intention is critical for seamless human-robot collaboration. This paper presents a novel shared control framework featuring planning-level intention prediction. A path replanning algorithm is designed to adjust the robot's desired trajectory according to inferred human intentions. To represent future motion intentions, we introduce the concept of an intention domain, which serves as a constraint for path replanning. The intention-domain prediction and path replanning problems are jointly formulated as a Markov Decision Process and solved through deep reinforcement learning. In addition, a Voronoi-based human trajectory generation algorithm is developed, allowing the model to be trained entirely in simulation without human participation or demonstration data. Extensive simulations and real-world user studies demonstrate that the proposed method significantly reduces operator workload and enhances safety, without compromising task efficiency compared with existing assistive teleoperation approaches.

Abstract

Robotic Manipulation

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