Robot Manipulation Learning and Interaction Control

Topic Information

Dear Colleagues,

This Topic, “Robot Manipulation Learning and Interaction Control”, aims to serve as a platform for advancing the theory and practice of robotic manipulation, control, and human–robot interaction. Building upon recent progress in robot learning, teleoperation, and adaptive control, this Topic seeks to address the growing need for intelligent, flexible, and robust robotic systems capable of performing complex manipulation tasks across diverse environments—from industry to homes and hospitals, etc. We hope to foster interdisciplinary collaboration among researchers, roboticists, engineers, and practitioners to share cutting-edge developments, novel methodologies, and real-world applications. Our objective is to catalyse research that advances the autonomy, adaptability, and safety of robotic manipulation and control systems, particularly in dynamic, unstructured, or human-centric settings. In this context, we welcome contributions that explore frameworks, models, algorithms, and systems related to robot learning, interaction control, and teleoperation. Topics of interest include (but are not limited to) the following:

• Learning-based manipulation and grasping strategies;
• Reinforcement learning and imitation learning for robot control;
• Teleoperation systems and shared autonomy;
• Visual–tactile perception for dexterous manipulation;
• Human-in-the-loop and collaborative control;
• Multimodal sensor fusion for manipulation tasks;
• Safety-aware and adaptive control in dynamic environments;
• Real-world deployment and benchmarking of manipulation systems;
• Robot learning from demonstration and simulation-to-reality transfer;
• Interaction-aware trajectory planning and motion generation.

We encourage researchers, engineers, and industry experts to contribute original research articles, review papers, and case studies that highlight recent advancements, innovative approaches, and practical insights within this rapidly evolving field.

Dr. Weiyong Si
Dr. Anqing Duan
Dr. Longnan Li
Prof. Dr. Chenguang Yang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400
Automation automation	2.9	4.5	2020	24.8 Days	CHF 1200
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400
Micromachines micromachines	3.5	7.1	2010	16.6 Days	CHF 2100
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600

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Published Papers (4 papers)

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23 pages, 3616 KB  

Open AccessArticle

Motion Planning-Augmented Hierarchical Reinforcement Learning for Long-Horizon Mobile Manipulation

by Hyungtai Kim and Mun-Taek Choi

Sensors 2026, 26(12), 3845; https://doi.org/10.3390/s26123845 - 17 Jun 2026

Viewed by 268

Abstract

Long-horizon mobile manipulation requires a robot to execute a sequence of heterogeneous subtasks such as navigation, picking, and articulated-object manipulation in indoor environments. Standard reinforcement learning suffers from reward sparsity and inefficient exploration in this setting, and hierarchical methods often fail at the [...] Read more.

Long-horizon mobile manipulation requires a robot to execute a sequence of heterogeneous subtasks such as navigation, picking, and articulated-object manipulation in indoor environments. Standard reinforcement learning suffers from reward sparsity and inefficient exploration in this setting, and hierarchical methods often fail at the hand-off between consecutive subtasks when the terminal state of one subtask is kinematically infeasible for the next. We propose a motion planning-augmented hierarchical reinforcement learning architecture to resolve the fundamental trade-offs between sample efficiency and hand-off reliability in long-horizon mobile manipulation. The mission is decomposed into subtasks via a Semi-Markov Decision Process; within each subtask, a collision-free reference trajectory generated by RRT* in the full joint configuration space is embedded into the reward as a per-step shaping signal; and a region-goal mechanism, defined analytically from inverse kinematics feasibility, replaces rigid coordinate hand-offs with a continuous feasible region. The architecture is evaluated in the ManiSkill-HAB simulation under teleport-free sequential execution and challenging initialization. The proposed method improves subtask success rate and sample efficiency over the baseline across all six evaluated subtasks, and the advantage compounds along the long-horizon task chain. Full article

(This article belongs to the Topic Robot Manipulation Learning and Interaction Control)

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30 pages, 6621 KB  

Open AccessArticle

One-Shot Box-Centric Teaching for Persistent Robotic Sorting-and-Filling with Relative Pose Constraints

by Wei Du and Jianhua Wu

Sensors 2026, 26(12), 3703; https://doi.org/10.3390/s26123703 - 10 Jun 2026

Viewed by 306

Abstract

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. [...] Read more.

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

(This article belongs to the Topic Robot Manipulation Learning and Interaction Control)

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27 pages, 6452 KB  

Open AccessArticle

Quaternion DMP with Controllable Final Angular Velocity for Robot Skill Generalization

by Xinhai Yao, Enzheng Zhang, Weijie Liao and Yihui Shen

Electronics 2026, 15(10), 2085; https://doi.org/10.3390/electronics15102085 - 13 May 2026

Viewed by 343

Abstract

Dynamic Movement Primitives (DMPs) are widely used for learning and generalizing robot skills. However, standard quaternion DMPs, when modeling orientation trajectories, constrain only the final orientation and cannot freely specify the final angular velocity. This limitation restricts its application to dynamic tasks requiring [...] Read more.

Dynamic Movement Primitives (DMPs) are widely used for learning and generalizing robot skills. However, standard quaternion DMPs, when modeling orientation trajectories, constrain only the final orientation and cannot freely specify the final angular velocity. This limitation restricts its application to dynamic tasks requiring precise boundary conditions, such as hitting or throwing. Although existing improved methods achieve velocity generalization to some extent, they often struggle to balance trajectory shape preservation with dynamic smoothness, frequently causing significant deviation from demonstrations or abrupt acceleration discontinuities. In this paper, we propose a novel robot skill generalization method that enables controllable final angular velocity for quaternion DMPs. Specifically, we construct a dynamic goal system driven by a quintic polynomial in Lie algebra space, analytically planning the target orientation’s evolution based on given multi-order boundary constraints. This mechanism not only achieves precise control over the final angular velocity but also inherently guarantees global C² continuous dynamics across primitive segments. Comparative simulations and real-world robot hitting experiments demonstrate that, compared to existing approaches, our proposed method effectively satisfies dynamic boundary constraints while exhibiting superior shape preservation, minimal trajectory deviation, and higher smoothness, thereby significantly improving skill generalization performance in complex dynamic tasks. Full article

(This article belongs to the Topic Robot Manipulation Learning and Interaction Control)

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16 pages, 3480 KB  

Open AccessArticle

Reinforcement Learning for Robot Assisted Live Ultrasound Examination

by Chenyang Li, Tao Zhang, Ziqi Zhou, Baoliang Zhao, Peng Zhang and Xiaozhi Qi

Electronics 2025, 14(18), 3709; https://doi.org/10.3390/electronics14183709 - 19 Sep 2025

Cited by 3 | Viewed by 2598

Abstract

Due to its portability, non-invasiveness, and real-time capabilities, ultrasound imaging has been widely adopted for liver disease detection. However, conventional ultrasound examinations heavily rely on operator expertise, leading to high workload and inconsistent imaging quality. To address these challenges, we propose a Robotic [...] Read more.

Due to its portability, non-invasiveness, and real-time capabilities, ultrasound imaging has been widely adopted for liver disease detection. However, conventional ultrasound examinations heavily rely on operator expertise, leading to high workload and inconsistent imaging quality. To address these challenges, we propose a Robotic Ultrasound Scanning System (RUSS) based on reinforcement learning to automate the localization of standard liver planes. It can help reduce physician burden while improving scanning efficiency and accuracy. The reinforcement learning agent employs a Deep Q-Network (DQN) integrated with LSTM to control probe movements within a discrete action space, utilizing the cross-sectional area of the abdominal aorta region as the criterion for standard plane determination. System performance was comprehensively evaluated against a target standard plane, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 24.51 dB and a Structural Similarity Index (SSIM) of 0.70, indicating high fidelity in the acquired images. Furthermore, a mean Dice coefficient of 0.80 for the abdominal aorta segmentation confirmed high anatomical localization accuracy. These preliminary results demonstrate the potential of our method for achieving consistent and autonomous ultrasound scanning. Full article

(This article belongs to the Topic Robot Manipulation Learning and Interaction Control)

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