Proactive slip control by learned slip model and trajectory adaptation

This paper presents a novel control approach to dealing with object slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipulation tasks. Existing works increase grip force to avoid/control slip. However, this may not be feasible when (i) the robot cannot increase the gripping force– the max gripping force is already applied or (ii) in- creased force damages the grasped object, such as soft fruit. Moreover, the robot fixes the gripping force when it forms a stable grasp on the surface of an object, and changing the gripping force during real-time manipulation may not be an effective control policy. We propose a novel control approach to slip avoidance including a learned action-conditioned slip predictor and a constrained optimiser avoiding a predicted slip given a desired robot action. We show the effectiveness of the proposed trajectory adaptation method with the receding horizon controller with a series of real-robot test cases. Our experimental results show our proposed data-driven predictive controller can control slip for objects unseen in training

Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking

Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pick a ripe fruit within a dense cluster. In such cases, the robot needs to safely push unripe fruits to reach a ripe one. Nonetheless, existing approaches to planning pushing movements in cluttered environments either are computationally expensive or only deal with 2-D cases and are not suitable for fruit picking, where it needs to compute 3- D pushing movements in a short time. In this work, we present a path planning algorithm for pushing occluding fruits to reach-and-pick a ripe one. Our proposed approach, called Interactive Probabilistic Movement Primitives (I-ProMP), is not computationally expensive (its computation time is in the order of 100 milliseconds) and is readily used for 3-D problems. We demonstrate the efficiency of our approach with pushing unripe strawberries in a simulated polytunnel. Our experimental results confirm I-ProMP successfully pushes table top grown strawberries and reaches a ripe one

Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach

Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous analyses on quasi-static pushing mechanics, and introduce a data-driven model that can accurately represent these mechan- ics and provide a prediction for the object’s inertial parameters. We evaluate the model with two datasets. For the first dataset, we set up a V-REP simulation of seven robots pushing objects with large range of inertial parameters, acquiring 48000 pushes in total. For the second dataset, we use the object pushes from the MIT M-Cube lab pushing dataset. We extract features from force, moment and velocity measurements of the pushes, and train a Multi-Output Regression Random Forest. The experimental results show that we can accurately predict the 2D inertial parameters from a single push, and that our method retains this robust performance under various surface types

Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re- grasp the needle using dual-arm movements. This yields an increased operator’s cognitive load, time-to-completion, fatigue and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability over the suturing trajectory. We evaluate the cost and its gradient on the needle grasping manifold that allows us to obtain the optimal grasping pose for joint-limit and singularity free movements of the needle during suturing. Then, we compute force cues that are applied to the Master Tool Manipulator (MTM) of the da Vinci to guide the operator towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration allowing the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of our proposed haptic- guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping

Towards Continuous Acoustic Tactile Soft Sensing

Acoustic Soft Tactile (AST) skin is a novel sensing technology that uses deformations of the acoustic channels beneath the sensing surface to predict static normal forces and their contact locations. AST skin functions by sensing the changes in the modulation of the acoustic waves travelling through the channels as they deform due to the forces acting on the skin surface. Our previous study tested different AST skin designs for three discrete sensing points and selected two designs that better predicted the forces and contact locations. This paper presents a study of the sensing capability of these two AST skin designs with continuous sensing points with a spatial resolution of 6 mm. Our findings indicate that the AST skin with a dual-channel geometry outperformed the single-channel type during calibration. The dual-channel design predicted more than 90% of the forces within a ± 3 N tolerance and was 84.2% accurate in predicting contact locations with ± 6 mm resolution. In addition, the dual-channel AST skin demonstrated superior performance in a real-time pushing experiment over an off-the-shelf soft tactile sensor. These results demonstrate the potential of using AST skin technology for real-time force sensing in various applications, such as human-robot interaction and medical diagnosis

Modular autonomous strawberry-picking robotic system

Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the elective harvesting of strawberries is a complicated robotic task forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, for example, picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g., high‐yielding and/or disease‐resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, for example, dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5‐degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localize strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new data sets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section

Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations

Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3% and 95.5% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4% and 90.3%, respectively

Planning maximum-manipulability cutting paths

This paper presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape but observes it from a noisy point cloud. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot’s manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g., demolition of the legacy nuclear plant, where the cut path needs not be precise as long as it achieves dismantling. We show how detours in the path can be leveraged to increase the manipulability of the robot at all points along the path. This helps to avoid singularities while maximising the robot’s capability to make small deviations during task execution. We show how a sampling-based planner can be projected onto the Riemannian manifold of a curved surface, and extended to include a term which maximises manipulability. We present the results of empirical experiments, with both simulated and real robots, which are tasked with moving over a variety of different surface shapes. Our planner enables successful task completion while ensuring significantly greater manipulability when compared against a conventional RRT* planner

A self-paced learning algorithm for change detection in synthetic aperture radar images

Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness

Haptic-guided Grasping to Minimise Torque Effort during Robotic Telemanipulation

Teleoperating robotic manipulators can be complicated and cognitively demanding for the human operator. Despite these difficulties, teleoperated robotic systems are still popular in several industrial applications, e.g., remote handling of hazardous material. In this context, we present a novel haptic shared control method for minimising the manipulator torque effort during remote manipulative actions in which an operator is assisted in selecting a suitable grasping pose for then displacing an object along a desired trajectory. Minimising torque is important because it reduces the system operating cost and extends the range of objects that can be manipulated. We demonstrate the effectiveness of the proposed approach in a series of representative real-world pick-and-place experiments as well as in human subjects studies. The reported results prove the effectiveness of our shared control vs. a standard teleoperation approach. We also find that haptic-only guidance performs better than visually guidance, although combining them together leads to the best overall results