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1. 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 of 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. 

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2. 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))

   null (Ed.)

   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 generator 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. 

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

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

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

   null (Ed.)

   bot 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 generator 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. 

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4. CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

   https://doi.org/10.1109/ICRA46639.2022.9811568  

   Wen, Bowen; Lian, Wenzhao; Bekris, Kostas E.; Schaal, Stefan (May 2022, IEEE International Conference on Robotics and Automation (ICRA))

   Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard to define and annotate. There is also yet no consensus on proper representations for modeling or off-the-shelf tools for performing task-relevant grasps. This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation. To achieve this, the entire framework is trained solely in simulation, including supervised training with synthetic label generation and self-supervised, hand-object interaction. In the context of this framework, this paper proposes a novel, object-centric canonical representation at the category level, which allows establishing dense correspondence across object instances and transferring task-relevant grasps to novel instances. Extensive experiments on task-relevant grasping of densely-cluttered industrial objects are conducted in both simulation and real-world setups, demonstrating the effectiveness of the proposed framework. 

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5. Formulation and Validation of an Intuitive QualityMeasure for Antipodal Grasp Pose Evaluation

   Tan, Tian; Alqasemi, Redwan; Dubey, Rajiv; Sarkar, Sudeep (September 2021, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   Practical robot applications that require grasping often fail due to failed grasp planning, and a good grasp quality measure is the key to successful grasp planning. In this paper, we developed a novel grasp quality measure that quantifies and evaluates grasp quality in real-time. To quantify the grasp quality, we compute a set of object movement features from analyzing the interaction between the gripper and the object’s projections in the image space. The normalizations and weights of the features are tuned to make practical and intuitive grasp quality predictions. To evaluate our grasp quality measure, we conducted a real robot grasping experiment with 1000 robot grasp trials on ten household objects to examine the relationship between our grasp scores and the actual robot grasping results. The results show that the average grasp success rate increases, and the average amount of undesired object movement decrease as the calculated grasp score increases, which validates our quality measure. We achieved a 100% grasp success rate from 100 grasps of the ten objects when using our grasp quality measure in planning top quality grasps. 

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