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1. Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

   https://doi.org/10.1109/iros40897.2019.8968263  

   Wu, Bohan ; Akinola, Iretiayo ; Allen, Peter K. ( November 2019 , 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces. However, leveraging this family of algorithms in multi-fingered robotic grasping remains a challenge due to large sim-to-real fidelity gaps and the high sample complexity of on-policy RL algorithms. This work aims to bridge these gaps by first reinforcement-learning a multi-fingered robotic grasping policy in simulation that operates in the pixel space of the input: a single depth image. Using a mapping from pixel space to Cartesian space according to the depth map, this method transfers to the real world with high fidelity and introduces a novel attention mechanism that substantially improves grasp success rate in cluttered environments. Finally, the direct-generative nature of this method allows learning of multi-fingered grasps that have flexible end-effector positions, orientations and rotations, as well as all degrees of freedom of the hand.
2. Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization

   A. Wu ; M. Guo ; C. K. Liu ( January 2022 , Conference on Robot Learning / Proceedings of Machine Learning Research)

   To fully utilize the versatility of a multi-fingered dexterous robotic hand for executing diverse object grasps, one must consider the rich physical constraints introduced by hand-object interaction and object geometry. We propose an integrative approach of combining a generative model and a bilevel optimization (BO) to plan diverse grasp configurations on novel objects. First, a conditional variational autoencoder trained on merely six YCB objects predicts the finger placement directly from the object point cloud. The prediction is then used to seed a nonconvex BO that solves for a grasp configuration under collision, reachability, wrench closure, and friction constraints. Our method achieved an 86.7% success over 120 real world grasping trials on 20 household objects, including unseen and challenging geometries. Through quantitative empirical evaluations, we confirm that grasp configurations produced by our pipeline are indeed guaranteed to satisfy kinematic and dynamic constraints. A video summary of our results is available at youtu.be/9DTrImbN99I.
3. 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 anymore »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
4. Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

   https://doi.org/10.1109/ICRA48891.2023.10161284  

   Chen, Yiye ; Lin, Yunzhi ; Xu, Ruinian ; Vela, Patricio A. ( May 2023 , International Conference on Robotics and Automation)

   The success of 6-DoF grasp learning with point cloud input is tempered by the computational costs resulting from their unordered nature and pre-processing needs for reducing the point cloud to a manageable size. These properties lead to failure on small objects with low point cloud cardinality. Instead of point clouds, this manuscript explores grasp generation directly from the RGB-D image input. The approach, called Keypoint-GraspNet (KGN), operates in perception space by detecting projected gripper keypoints in the image, then recovering their SE(3) poses with a PnP algorithm. Training of the network involves a synthetic dataset derived from primitive shape objects with known continuous grasp families. Trained with only single-object synthetic data, Keypoint-GraspNet achieves superior result on our single-object dataset, comparable performance with state-of-art baselines on a multi-object test set, and outperforms the most competitive baseline on small objects. Keypoint-GraspNet is more than 3x faster than tested point cloud methods. Robot experiments show high success rate, demonstrating KGN's practical potential.
5. Dynamic-GAN: Learning Spatial-Temporal Attention for Dynamic Object Removal in Feature Dense Environments

   https://doi.org/10.1109/IROS47612.2022.9981102  

   Trombley, Christopher M ; Kumar Das, Sumit ; Popa, Dan O ( October 2022 , Dynamic-GAN: Learning Spatial-Temporal Attention for Dynamic Object Removal in Feature Dense Environments)

   This paper presents an attention-based, deep learning framework that converts robot camera frames with dynamic content into static frames to more easily apply simultaneous localization and mapping (SLAM) algorithms. The vast majority of SLAM methods have difficulty in the presence of dynamic objects appearing in the environment and occluding the area being captured by the camera. Despite past attempts to deal with dynamic objects, challenges remain to reconstruct large, occluded areas with complex backgrounds. Our proposed Dynamic-GAN framework employs a generative adversarial network to remove dynamic objects from a scene and inpaint a static image free of dynamic objects. The Dynamic-GAN framework utilizes spatial-temporal transformers, and a novel spatial-temporal loss function. The evaluation of Dynamic-GAN was comprehensively conducted both quantitatively and qualitatively by testing it on benchmark datasets, and on a mobile robot in indoor navigation environments. As people appeared dynamically in close proximity to the robot, results showed that large, feature-rich occluded areas can be accurately reconstructed with our attention-based deep learning framework for dynamic object removal. Through experiments we demonstrate that our proposed algorithm has up to 25% better performance on average as compared to the standard benchmark algorithms.