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1. Workspace Aware Online Grasp Planning

   https://doi.org/10.1109/IROS.2018.8593644  

   Akinola, Iretiayo; Varley, Jacob; Chen, Boyuan; Allen, Peter K. (October 2018, Proceedings Article published Oct 2018 in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This work provides a framework for a workspace aware online grasp planner. This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process. Offline, a database of hundreds of thousands of unique end-effector poses were queried for feasibility. At runtime, our grasp planner uses this database to bias the hand towards reachable end-effector configurations. The bias keeps the grasp planner in accessible regions of the planning scene so that the resulting grasps are tailored to the situation at hand. This results in a higher percentage of reachable grasps, a higher percentage of successful grasp executions, and a reduced planning time. We also present experimental results using simulated and real environments. 

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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 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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3. 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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4. Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network

   Lu, Qingkai; Chenna, Kautilya; Sundaralingam, Balakumar; Hermans, Tucker (January 2017, International Symposium on Robotics Research)

   We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a function of both visual information of an object and grasp configuration. We can then formulate grasp planning as inferring the grasp configu- ration which maximizes the probability of grasp success. We efficiently perform this inference using a gradient-ascent optimization inside the neural network using the backpropagation algorithm. Our work is the first to directly plan high quality multi- fingered grasps in configuration space using a deep neural network without the need of an external planner. We validate our inference method performing both multi- finger and two-finger grasps on real robots. Our experimental results show that our planning method outperforms existing planning methods for neural networks; while offering several other benefits including being data-efficient in learning and fast enough to be deployed in real robotic applications. 

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5. 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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