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1. A Framework for Robot Grasp Transferring with Non-rigid Transformation

   https://doi.org/10.1109/IROS.2018.8593668  

   Lin, Hsien-Chung; Tang, Te; Fan, Yongxiang; Tomizuka, Masayoshi (January 2019, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   Grasp planning is essential for robots to execute dexterous tasks. Solving the optimal grasps for various objects online, however, is challenging due to the heavy computation load during exhaustive sampling, and the difficulties to consider task requirements. This paper proposes a framework to combine analytic approach with learning for efficient grasp generation. The example grasps are taught by human demonstration and mapped to similar objects by a non-rigid transformation. The mapped grasps are evaluated analytically and refined by an orientation search to improve the grasp robustness and robot reachability. The proposed approach is able to plan high-quality grasps, avoid collision, satisfy task requirements, and achieve efficient online planning. The effectiveness of the proposed method is verified by a series of experiments. 

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2. 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 as 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. 

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3. 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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4. Grasping Objects Big and Small: Human Heuristics Relating Grasp-Type and Object Size

   https://doi.org/10.1109/icra.2018.8460860  

   Kothari, Ammar; Morrow, John; Thrasher, Victoria; Engle, Kadon; Balasubramanian, Ravi; Grimm, Cindy (May 2018, IEEE International Conference on Robotics and Automation)

   This paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size. 

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5. Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter

   https://doi.org/10.1007/s10514-020-09907-y  

   Wu, Bohan; Akinola, Iretiayo; Gupta, Abhi; Xu, Feng; Varley, Jacob; Watkins-Valls, David; Allen, Peter K. (February 2020, Autonomous Robots)

   Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingered robot hands. This framework introduces a novel attention mechanism that substantially improves the grasp success rate in clutter. Its generative nature allows the learning of full-DOF grasps with flexible end-effector positions and orientations, as well as all finger joint angles of the hand. Trained purely in simulation, this framework skillfully closes the sim-to-real gap. To close the visual sim-to-real gap, this framework uses a single depth image as input. To close the dynamics sim-to-real gap, this framework circumvents continuous motor control with a direct mapping from pixel to Cartesian space inferred from the same depth image. Finally, this framework demonstrates inter-robot generality by achieving over 92% real-world grasp success rates in cluttered scenes with novel objects using two multi-fingered robotic hand-arm systems with different degrees of freedom. 

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