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1. A Deep Learning-Based Autonomous Robot Manipulator for Sorting Application

   https://doi.org/10.1109/IRC.2020.00055  

   Bui, Hoang-Dung ; Nguyen, Hai ; La, Hung Manh ; Li, Shuai ( November 2020 , 2020 Fourth IEEE International Conference on Robotic Computing (IRC))

   Robot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to development of wide-range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation and object sorting tasks. The proposed approach can also handle grasping on certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generatormore »algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp will be chosen to execute on a real robot. A motion planner will generate collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance.« less
2. Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects

   Danielczuk, Michael ; Balakrishna, Ashwin ; Brown, Daniel ; Devgon, Shivin ; Goldberg, Ken ( January 2020 , 4th Conference on Robot Learning)

   There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consis- tently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps. Moti- vated by such objects, we propose a novel problem setting, Exploratory Grasping, for efficiently discovering reliable grasps on an unknown polyhedral object via sequential grasping, releasing, and toppling. We formalize Exploratory Grasping as a Markov Decision Process where we assume that the robot can (1) distinguish stable poses of a polyhedral object ofmore »unknown geometry, (2) generate grasp can- didates on these poses and execute them, (3) determine whether each grasp is successful, and (4) release the object into a random new pose after a grasp success or topple the object after a grasp failure. We study the theoretical complexity of Exploratory Grasping in the context of reinforcement learning and present an efficient bandit-style algorithm, Bandits for Online Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the problem to efficiently discover high performing grasps for each object stable pose. BORGES can be used to complement any general-purpose grasping algorithm with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn policies for objects in which they exhibit persistent failures. Simulation experiments suggest that BORGES can significantly outperform both general-purpose grasping pipelines and two other online learning algorithms and achieves performance within 5% of the optimal policy within 1000 and 8000 timesteps on average across 46 challenging objects from the Dex-Net adversarial and EGAD! object datasets, respectively. Initial physical experiments suggest that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with just 200 grasp attempts in the real world. See https://tinyurl.com/exp-grasping for supplementary material and videos.« less
3. Tri-modal thin-film flexible electronic skin to augment robotic grasping

   https://doi.org/10.1109/MEMSYS.2018.8346698  

   Yu, Caroline ; Cavallari, Marco R. ; Kymissis, Ioannis ( January 2018 , MEMS 2018)

   Robotic grasping is successful when a robot can sense and grasp an object without letting it slip. Beyond industrial robotic tasks, there are two main robotic grasping methods. The first is planning-based grasping where the object geometry is known beforehand and stable grasps are calculated using algorithms [1]. The second uses tactile feedback. Currently, there are capacitive sensors placed beneath stiff pads on the front of robotic fingers [2]. With post-execution grasp adjustment procedures to estimate grasp stability, a support vector machine classifier can distinguish stable and unstable grasps. The accuracy across the classes of tested objects is 81% [1].more »We are proposing to improve the classifier's accuracy by wrapping flexible sensors around the robotic finger to gain information from the edges and sides of the finger.« less
4. Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter

   https://doi.org/10.1109/IROS51168.2021.9636876  

   Corsaro, M ; Tellex, S ; Konidaris, GD ( September 2021 , Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems)

   We propose an approach to multi-modal grasp detection that jointly predicts the probabilities that several types of grasps succeed at a given grasp pose. Given a partial point cloud of a scene, the algorithm proposes a set of feasible grasp candidates, then estimates the probabilities that a grasp of each type would succeed at each candidate pose. Predicting grasp success probabilities directly from point clouds makes our approach agnostic to the number and placement of depth sensors at execution time. We evaluate our system both in simulation and on a real robot with a Robotiq 3-Finger Adaptive Gripper and comparemore »our network against several baselines that perform fewer types of grasps. Our experiments show that a system that explicitly models grasp type achieves an object retrieval rate 8.5% higher in a complex cluttered environment than our highest-performing baseline.« less
5. Dynamic Grasping with Reachability and Motion Awareness

   Iretiayo Akinola, Jingxi Xu ( January 2021 , iros)

   Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning needs to be adaptive and in real time, the delay in computation makes prediction necessary. In this paper, we present a dynamic grasping framework that is reachabilityaware and motion-aware. Specifically, we model the reachability space of the robot using a signed distance field which enables us to quickly screen unreachable grasps. Also, we train a neural network to predict the grasp quality conditioned on the current motion of the target. Using these asmore »ranking functions, we quickly filter a large grasp database to a few grasps in real time. In addition, we present a seeding approach for arm motion generation that utilizes solution from previous time step. This quickly generates a new arm trajectory that is close to the previous plan and prevents fluctuation. We implement a recurrent neural network (RNN) for modelling and predicting the object motion. Our extensive experiments demonstrate the importance of each of these components and we validate our pipeline on a real robot.« less