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Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and robust generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning strategies. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach uses a high-level policy for task planning and a low-level policy for generating low-level trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and help recover from errors made by the low-level policy. We validated our framework through ex vivo experiments on cholecystectomy, a commonly practiced minimally invasive procedure, and conducted ablation studies to evaluate key components of the system. Our method achieves a 100% success rate across eight different ex vivo gallbladders, operating fully autonomously without human intervention. The hierarchical approach improved the policy's ability to recover from suboptimal states that are inevitable in the highly dynamic environment of realistic surgical applications. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems.

Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.

Mobile robots perform fetch-and-carry tasks autonomously. An intelligent, sensor-equipped mobile robot does not require dedicated pathways or extensive facility modification. In the hospital, mobile robots can be used to carry specimens, pharmaceuticals, meals, etc. between supply centers, patient areas, and laboratories. The HelpMate (Transitions Research Corp.) mobile robot was developed specifically for hospital environments. To reach a desired destination, Help-Mate navigates with an on-board computer that continuously polls a suite of sensors, matches the sensor data against a pre-programmed map of the environment, and issues drive commands and path corrections. A sender operates the robot with a user-friendly menu that prompts for payload insertion and desired destination(s). Upon arrival at its selected destination, the robot prompts the recipient for a security code or physical key and awaits acknowledgement of payload removal. In the future, the integration of HelpMate with robot manipulators, test equipment, and central institutional information systems will open new applications in more localized areas and should help overcome difficulties in filling transport staff positions.

Medical robots have evolved from autonomous systems to tele-operated platforms and mechanically-grounded, cooperatively-controlled robots. Whilst these approaches have seen both commercial and clinical success, uptake of these robots remains moderate because of their high cost, large physical footprint and long setup times. More recently, researchers have moved toward developing hand-held robots that are completely ungrounded and manipulated by surgeons in free space, in a similar manner to how conventional instruments are handled. These devices provide specific functions that assist the surgeon in accomplishing tasks that are otherwise challenging with manual manipulation. Hand-held robots have the advantages of being compact and easily integrated into the normal surgical workflow since there is typically little or no setup time. Hand-held devices can also have a significantly reduced cost to healthcare providers as they do not necessitate the complex, multi degree-of-freedom linkages that grounded robots require. However, the development of such devices is faced with many technical challenges, including miniaturization, cost and sterility, control stability, inertial and gravity compensation and robust instrument tracking. This review presents the emerging technical trends in hand-held medical robots and future development opportunities for promoting their wider clinical uptake.

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

In general surgery, the use of robotic and laparoscopic methods has increased. Robotic surgery that requires the least incision has advanced over the years in a short period of time, benefitting both the patient and the surgeon. According to this, robotic platforms and tools are now being used and improved more commonly in general surgery. In a quickly growing and dynamic environment of research and development, the goal of this review is to explore the present and emerging surgical robotic technologies. Future progress in robotics will focus primarily on more durable haptic systems that would provide tactile and kinesthetic input, miniaturisation and micro-robotics, better visual feedback with higher fidelity detail and magnification, and autonomous robots. It is recommended to develop a structured training course with benchmarks for success and evidence-based training strategies. This usually includes a step-by-step progression starting with observation, case aid in programming and manipulation of surgical instruments, learning the basics of robotics in a dry and wet lab setting, attaining non-technical skills on an individual and team level, and monitored modular console training, accompanied by autonomous practice. Prior to independent practice, basic robotics skills and procedural activities must be performed safely and effectively as part of robotic surgical training. It is advised to create a systematic training programme with performance indicators and research-based instructional techniques.

Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. Here, we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks, including electromagnetic (EM) field manipulations and interactions with robots and humans. Leveraging recently released foundation models, metaAgent reasons in high-level natural language, acting upon diverse prompts from an evolving complex environment. Specifically, metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism, where agents are domain experts in sensing, planning, grounding, and coding. In response to live environmental feedback within a real-world setting emulating an ambient-assisted living context (including human requests in natural language), our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a robot. metaAgent masters foundational EM manipulation skills related to wireless communications and sensing, and it memorizes and learns from past experience based on human feedback.

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.

Using robots for tomato truss harvesting represents a promising approach to agricultural production. However, incomplete acquisition of perception information and clumsy operations often results in low harvest success rates or crop damage. To addressthis issue, we designed a new method for tomato truss perception, an autonomous harvesting method, and a novel circular rotary cutting end-effector. The robot performs object detection and keypoint detection on tomato trusses using the proposed topdown fusion network, making decisions on suitable targets for harvesting based on phenotyping and pose estimation. The designed end-effector moves gradually from the bottom up to wrap around the tomato truss, cutting the peduncle to complete the harvest. Experiments conducted in real-world scenarios for robotic perception and autonomous harvesting of tomato trusses show that the proposed method increases accuracy by up to 11.42 and 22.29 for complete and limited dataset conditions, compared to baseline models. Furthermore, we have implemented an automatic tomato harvesting system based on TDFNet, which reaches an average harvest success rate of 89.58 in the greenhouse.

Autonomous manipulation operations represent the high intelligent coordination from robotic vision and control, it is also a symbol of the advances of robotic intelligence. The limitations of visual sensing and the increasingly complex experimental conditions make autonomous manipulation operations more difficult, particularly for deep reinforcement learning methods, which can enhance robotic control intelligence but require a lot of training process. Due to the high-dimensional continuous state space and continuous action space characteristics of underwater operations, this paper adopts a policy-based reinforcement learning method as the foundational approach. To address the issues of instability and low convergence efficiency in traditional policy-based reinforcement learning algorithms during the learning process, this paper proposes a novel policy learning method. This method adopts the Proximal Policy Optimization algorithm (PPOClip) and optimizes it through an actor-critic network. The aim is to improve the stability and effectiveness of convergence in the learning process. In the underwater training environment, a new reward shaping scheme has been designed to address the issue of reward sparsity during the training process. The manually crafted dense reward function is utilized as attractive and repulsive potential functions for goal manipulation and obstacle avoidance. On the highly complex underwater manipulation and training environment, transferred learning algorithm has been established to reduce the training times and compensate the differences between the simulation and experiment. Simulations and tank experiments have verified the performance of the proposed strategy learning method.

The increasing complexity and diversity of laboratory automation call for more adaptable and integrated solutions. This paper presents a real-life implementation of the Laboratory Automation Plug and Play (LAPP) concept, leveraging the SiLA 2 protocol to enable seamless robotic integration in heterogeneous laboratory environments. We introduce the mobERT mobile manipulator, a collaborative robot system designed to handle standard labware, such as ANSI/SLAS microtiter plates, across multiple workstations. Our approach employs a hierarchical workflow decomposition and a system architecture model to facilitate plug-and-play configuration. We implemented this system using Biosero's GBG scheduler, ensuring scalable and standardized interoperability. The implementation demonstrates the practical application of LAPP in a pharmaceutical laboratory setting, specifically automating the sample preparation workflow for High-performance Liquid Chromatography (HPLC). This work highlights the feasibility of modular, low-level control agnostic solutions in advancing laboratory automation towards higher efficiency and flexibility.

Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been restricted to one specific scene, primarily due to the difficulty of acquiring generalizable skills and the expensiveness of in-the-wild humanoid robot data. In this work, we build a real-world robotic system to address this challenging problem. Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor, and 3) an improved 3D Diffusion Policy learning algorithm for humanoid robots to learn from noisy human data. We run more than 2000 episodes of policy rollouts on the real robot for rigorous policy evaluation. Empowered by this system, we show that using only data collected in one single scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios. Videos are available at https://humanoid-manipulation.github.io .

In order to explore robotic grasping in unstructured and dynamic environments, this work addresses the visual perception phase involved in the task. This phase involves the processing of visual data to obtain the location of the object to be grasped, its pose and the points at which the robot`s grippers must make contact to ensure a stable grasp. For this, the Cornell Grasping dataset is used to train a convolutional neural network that, having an image of the robot`s workspace, with a certain object, is able to predict a grasp rectangle that symbolizes the position, orientation and opening of the robot`s grippers before its closing. In addition to this network, which runs in real-time, another one is designed to deal with situations in which the object moves in the environment. Therefore, the second network is trained to perform a visual servo control, ensuring that the object remains in the robot`s field of view. This network predicts the proportional values of the linear and angular velocities that the camera must have so that the object is always in the image processed by the grasp network. The dataset used for training was automatically generated by a Kinova Gen3 manipulator. The robot is also used to evaluate the applicability in real-time and obtain practical results from the designed algorithms. Moreover, the offline results obtained through validation sets are also analyzed and discussed regarding their efficiency and processing speed. The developed controller was able to achieve a millimeter accuracy in the final position considering a target object seen for the first time. To the best of our knowledge, we have not found in the literature other works that achieve such precision with a controller learned from scratch. Thus, this work presents a new system for autonomous robotic manipulation with high processing speed and the ability to generalize to several different objects.

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of 1) performing tasks that always require contact and 2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks.

Large Language Models (LLMs) are gaining popularity in the field of robotics. However, LLM-based robots are limited to simple, repetitive motions due to the poor integration between language models, robots, and the environment. This paper proposes a novel approach to enhance the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC). The approach involves using a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. The system also employs a YOLO-based perception algorithm, providing visual cues to the LLM, which aids in planning feasible motions within the specific environment. Additionally, an HRC method is proposed by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance. Real-world experiments have been conducted using the Toyota Human Support Robot for manipulation tasks. The outcomes indicate that tasks requiring complex trajectory planning and reasoning over environments can be efficiently accomplished through the incorporation of human demonstrations.

Human-robot interaction will play an essential role in various industries and daily tasks, enabling robots to effectively collaborate with humans and reduce their physical workload. Most of the existing approaches for physical human-robot interaction focus on collaboration between a human and a single ground or aerial robot. In recent years, very little progress has been made in this research area when considering multiple aerial robots, which offer increased versatility and mobility. This paper proposes a novel approach for physical human-robot collaborative transportation and manipulation of a cable-suspended payload with multiple aerial robots. The proposed method enables smooth and intuitive interaction between the transported objects and a human worker. In the same time, we consider distance constraints during the operations by exploiting the internal redundancy of the multi-robot transportation system. The key elements of our approach are (a) a collaborative payload external wrench estimator that does not rely on any force sensor; (b) a 6D admittance controller for human-aerial-robot collaborative transportation and manipulation; (c) a human-aware force distribution that exploits the internal system redundancy to guarantee the execution of additional tasks such inter-human-robot separation without affecting the payload trajectory tracking or quality of interaction. We validate the approach through extensive simulation and real-world experiments. These include scenarios where the robot team assists the human in transporting and manipulating a load, or where the human helps the robot team navigate the environment. We experimentally demonstrate for the first time, to the best of our knowledge, that our approach enables a quadrotor team to physically collaborate with a human in manipulating a payload in all 6 DoF in collaborative human-robot transportation and manipulation tasks.

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.

We describe the software components of a robotics system designed to autonomously grasp objects and perform dexterous manipulation tasks with only high-level supervision. The system is centered on the tight integration of several core functionalities, including perception, planning and control, with the logical structuring of tasks driven by a Behavior Tree architecture. The advantage of the implementation is to reduce the execution time while integrating advanced algorithms for autonomous manipulation. We describe our approach to 3-D perception, real-time planning, force compliant motions, and audio processing. Performance results for object grasping and complex manipulation tasks of in-house tests and of an independent evaluation team are presented.

This paper is devoted to a system for aerial manipulation, composed of a helicopter and an industrial manipulator. The usage of an industrial manipulator is motivated by practical applications which were identified in different cooperation projects with the industry. We address the coupling between manipulator and helicopter and show that even in case when we have an ideal controller for manipulator and a highperformance controller for helicopter, an unbounded energy flow can be generated by internal forces between helicopter and manipulator if both controllers are used independently. To solve this problem we propose a new kinematical coupling for control by introducing an additional manipulation DoF realized by helicopter rotation around its yaw axis. The new experimental setup and required modifications in the manipulator controller for this purpose are described. Further, we propose dynamical coupling which is implemented by modification of the helicopter controller feeding the interaction force/torque, measured between manipulator base and fuselage, directly to the actuators of the rotor blades. At the end, we present experimental results for aerial manipulation and their analysis.