In this paper, an adaptive force-motion control framework with orientation compliance is present for redundant manipulators in physical interaction with unknown surfaces. The proposed framework includes control task space definition and double-closed-loop control based on external force loop approach. Firstly, a specification matrix is designed merely through force feedback to ensure the control task space defined in orthogonal spaces. Then, an orientation compliance controller and a force-motion close-loop controller are constructed in the outer-loop control of external force feedback loop approach. Secondly, the output of outer-loop control task, along with boundary constraints and optimization indexes is formulated as a nolinear dynamic programming problem. Next a finite-time convergence neural network based inner-loop controller is proposed for this category of dynamic programming problem and its stability and convergence analysis are given. Simulations verify the convergence and effectiveness of the proposed framework. The real-world experiments show that the Mean Integral of the Absolute Error of the proposed control framework is reduced by 77.26% compared with constant impedance control.

A framework is proposed in this paper for learning variable impedance in percutaneous puncture surgery, with the aim of simplifying the robotic puncture of soft tissues. The framework involves simulating the dynamic changes that occur when the human arm interacts with human tissues and transferring the resulting adaptive capabilities to the robot through learning movement trends and stiffness changes. To enhance performance during task execution, we integrate the variable impedance control framework with the interactive operation and feedback controllers. To provide flexibility for trajectory modification during operation, derivative Gaussian processes are introduced to identify the target position and obtain a model of motion trends. This control law is combined with virtual dynamics that describe puncture dynamics, enabling the robot to regulate interactions and plan its trajectory. We present experiments involving tissue puncturing tasks performed by the Franka-Emika Panda robot with varying degrees of hardness. The results demonstrate that our framework is capable of learning manipulation skills for physical interaction with humans, thereby reducing application complexity in tasks involving complex force interactions for robots. Compared to using fixed or variable impedance gain controllers, our approach effectively improves the success rate, stability, and efficiency of percutaneous puncture. Note to Practitioners—This paper is motivated by the limitations encountered by robots when handling deformed objects. In traditional robot control processes, the assumption of a fixed and unchanging contact object poses a significant challenge in applying robot control to the medical industry. Consequently, it becomes imperative for robot control systems to develop stable intelligent approaches capable of interacting with deformed objects. In this paper, we propose a framework for robot-assisted puncture that combines robotic impedance control techniques with sensing mechanisms. By integrating these approaches, our framework demonstrates effectiveness in performing tasks involving soft tissues with varying levels of hardness. Our proposed method encompasses three main ideas: 1) Sensing muscle activity during task execution enables the acquisition of task parameters from the human arm. 2) The utilization of a robot control method enhances the stability of the robot’s execution process. 3) The proposed method shows potential for application in processing and treating objects with low stiffness, deformed objects, and thin-walled parts. Experimental results validate the effectiveness of the developed method. In future work, it is important for the robot-assisted puncture system to consider recognizing and localizing more diverse targets to enhance its generalization capabilities.

This paper proposes the distance- and velocity-based simultaneous obstacle avoidance and target tracking (DV-SOATT) method for the trajectory tracking problem of multiple wheeled mobile robots (MWMRs) operating in a shared workspace based on the relative positions and velocities of the wheeled mobile robots (WMRs) and their encountered obstacles. Compared to the previous arts considered only their relative positions, the DV-SOATT method that adds an auxiliary velocity vector lessens needless activation of the collision avoidance maneuvers, where the DV-SOATT introduces radial bounds for forecasting a collision. We provide two decision criteria for the addition of the auxiliary velocity term and compare the DV-SOATT method with the original method proposed by Li et al. (2021). The problem of the WMRs pause from the path conflict is addressed. Bound constraints on MWMRs’ velocities are considered to restrict the movement speed of the robot so as to ensure smoothness. The control law is built on Lagrange multipliers on basis of constructing a quadratic programming problem. Slack variables are discarded. Bound constraints on optimization variables are included in the piecewise-linear projection function. The stability of the control law, together with the efficiency of the DV-SOATT method, is discussed based on the Lyapunov function. The efficiency is tested on multiple omnidirectional Mecanum-wheeled mobile robots and validated through physical experiments and simulation.

Stroke survivors usually have dyskinesia, who have an urgent need for rehabilitation-assist training. To reduce the labor of rehabilitation therapists, this paper attempts to investigate an effective rehabilitation-assisted robot skill acquisition framework, which is inspired by the scheme of robot learning from demonstration (LfD). Since most of the current LfD methods were implemented with rigorous assumptions that the considering motion features are only represented on an individual manifold. Meanwhile, despite many advancements that have been achieved on time-position trajectories and position-velocity trajectories, those methods are restricted to Euclidean space and can not be applied to learn those dexterous and compliant rehabilitation-assisted robot skills such as position-orientation trajectories and force-stiffness trajectories, etc. In this paper, we propose a novel skill acquisition framework for rehabilitation-assisted robot using manifold-mappings and Gaussian processes, which allows the robot to 1) simultaneously considering the robot position, orientation, force as well as stiffness by manifold-mappings among d-dimensional Euclidean space$\mathcal{R}^{d}$, special orthogonal group$S\mathcal{O}$(3), and Riemannian space$\mathcal{M}$, respectively, which resulting in accurate motion and compliant behavior; 2) retrieving skill representation by encap-sulating the variability of multiple high-dimensional demon-strations that with input-dependent noises; 3) implementing the via-points-based trajectory modulation by considering task constraints or environmental changes. To simplify the writing, we named the proposed framework as Multi-motion Features Fusion-based Robot Skill Learning (MF2RoSL). To effectively evaluate the effectiveness of our proposed method, an upper limb rehabilitation training system with a collaborative Kinova robot is developed. The training exercises of our system are determined according to the Brunnstrom therapeutic approach to the management of hemiplegic patients, including the 3-DoFs movement of the shoulder joint and a 7-DoF movement of an insertion/extraction task for assessing the activities of daily living (ADL). Results indicate that our proposed MF2RoSL method allows the robot to learn rehabilitation skills from the therapist and can be rapidly adapted to new patients.

Robot Learning from humans is a promising paradigm for directly transferring human skills to robots. This learning allows robots to encapsulate task constraints and motion patterns from human demonstrations as well as acquire skills that can be adapted to unseen scenarios. Even though many state-of-the-art skill-learning successes have been achieved, simultaneously addressing variability from a complex and long-horizon manipulation task and generalizing it to external uncertainty remains a challenge. This efficient skill learning has to allow for handling large-scale, high-dimensional demonstrations, adapting to environmental changes (starting, via and end points and obstacles), generalizing to task constraints (trajectory precision, stiffness), and measuring uncertainty in the reproduction. To this effect, we present a novel robot skill-learning framework called SVGP-CoGP that will implement all the aforementioned properties by encoding task variability from multiple demonstrations using Sparse Variational Gaussian Processes (SVGP) and adapting to additional constraints via a coregionalized multi-output GP (CoGP) based on SVGP. The proposed method can significantly reduce the computational complexity of model fitting by making use of the variational inference of GP models, which makes it possible for robots to learn skills from complex and long-horizon tasks. We evaluated and compared the effectiveness and strengths of our framework with existing probabilistic methods on a Kinova robot that performed emergency button-pressing tasks. The results indicated that our framework allowed the robot to learn skills from complex and long-horizon manipulation tasks that outperformed baselines both in quantitative evaluation and in an online test. Note to Practitioners–The objective of this work is to address the problem of robot-learning from complex and long-horizon manipulation tasks to allow end-users to teach robots new tasks by having them learn from human demonstrations instead of being programmed. We start with a brief historical overview of widely used methods and summarize five prominent capabilities that a skill-learning approach should have: variability, uncertainty, correlation, extrapolation, and adaptability. We then propose an entirely GP-based skill-learning framework by simultaneously addressing all those capabilities by using a sparse variational Gaussian process (SVGP) in conjunction with a coregionalized multioutput GP model. The proposed framework incorporated variational inference and kernel treatments such that the robot learned skills from large-scale demonstrations and high-dimensional trajectories. Finally, experimental evaluation and performance comparisons were performed in a real robot button-pressing task, the results of which indicated that our proposed method enables robots to achieve complex and long-horizon manipulation tasks in dynamic and unstructured environments. With the rapid development of collaborative robots in service and industry, our findings have application scenarios as diverse as robot learning from demonstration, robot skill learning, human–robot collaboration, and other complex and long-horizon manipulation tasks.

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.