We consider the problem of closed-loop robotic grasping and present a novel planner which uses Visual Feedback and an uncertainty-aware Adaptive Sampling strategy (VFAS) to close the loop. At each iteration, our method VFAS-Grasp builds a set of candidate grasps by generating random perturbations of a seed grasp. The candidates are then scored using a novel metric which combines a learned grasp-quality estimator, the uncertainty in the estimate and the distance from the seed proposal to promote temporal consistency. Additionally, we present two mechanisms to improve the efficiency of our sampling strategy: We dynamically scale the sampling region size and number of samples in it based on past grasp scores. We also leverage a motion vector field estimator to shift the center of our sampling region. We demonstrate that our algorithm can run in real time (20 Hz) and is capable of improving grasp performance for static scenes by refining the initial grasp proposal. We also show that it can enable grasping of slow moving objects, such as those encountered during human to robot handover. Video: https://youtu.be/8DRe2OFlf7o

A good representation of a large, complex mobile robot workspace must be space-efficient yet capable of encoding relevant geometric details. When exploring unknown environments, it needs to be updatable incrementally in an online fashion. We introduce HIO-SDF, a new method that represents the environment as a Signed Distance Field (SDF). State of the art representations of SDFs are based on either neural networks or voxel grids. Neural networks are capable of representing the SDF continuously. However, they are hard to update incrementally as neural networks tend to forget previously observed parts of the environment unless an extensive sensor history is stored for training. Voxel-based representations do not have this problem but they are not space-efficient especially in large environments with fine details. HIO-SDF combines the advantages of these representations using a hierarchical approach which employs a coarse voxel grid that captures the observed parts of the environment together with high-resolution local information to train a neural network. HIO-SDF achieves a 46% lower mean global SDF error across all test scenes than a state of the art continuous representation, and a 30% lower error than a discrete representation at the same resolution as our coarse global SDF grid. Videos and code are available at: https://samsunglabs.github.io/HIO-SDF-project-page/

The choice of a grasp plays a critical role in the success of downstream manipulation tasks. Consider a task of placing an object in a cluttered scene; the majority of possible grasps may not be suitable for the desired placement. In this paper, we study the synergy between the picking and placing of an object in a cluttered scene to develop an algorithm for task-aware grasp estimation. We present an object-centric action space that encodes the relationship between the geometry of the placement scene and the object to be placed in order to provide placement affordance maps directly from perspective views of the placement scene. This action space enables the computation of a one-to-one mapping between the placement and picking actions allowing the robot to generate a diverse set of pick-and-place proposals and to optimize for a grasp under other task constraints such as robot kinematics and collision avoidance. With experiments both in simulation and on a real robot we demonstrate that with our method, the robot is able to successfully complete the task of placement-aware grasping with over 89 % accuracy in such a way that generalizes to novel objects and scenes.

In this paper, we present a realtime method for simultaneous object-level scene understanding and grasp prediction. Specifically, given a single RGBD image of a scene, our method localizes all the objects in the scene and for each object, it generates the following: full 3D shape, scale, pose with respect to the camera frame, and a dense set of feasible grasps. The main advantage of our method is its computation speed as it avoids sequential perception and grasp planning. With detailed quantitative analysis of reconstruction quality and grasp accuracy, we show that our method delivers competitive performance compared to the state-of-the-art methods, while providing fast inference at 30 frames per second speed.

We introduce Reactive Action and Motion Planner (RAMP), which combines the strengths of sampling-based and reactive approaches for motion planning. In essence, RAMP is a hierarchical approach where a novel variant of a Model Predictive Path Integral (MPPI) controller is used to generate trajectories which are then followed asynchronously by a local vector field controller. We demonstrate, in the context of a table clearing application, that RAMP can rapidly find paths in the robot's configuration space, satisfy task and robot-specific constraints, and provide safety by reacting to static or dynamically moving obstacles. RAMP achieves superior performance through a number of key innovations: we use Signed Distance Function (SDF) representations directly from the robot configuration space, both for collision checking and reactive control. The use of SDFs allows for a smoother definition of collision cost when planning for a trajectory, and is critical in ensuring safety while following trajectories. In addition, we introduce a novel variant of MPPI which, combined with the safety guarantees of the vector field trajectory follower, performs incremental real-time global trajectory planning. Simulation results establish that our method can generate paths that are comparable to traditional and state-of-the-art approaches in terms of total trajectory length while being up to 30 times faster. Real-world experiments demonstrate the safety and effectiveness of our approach in challenging table clearing scenarios. Videos and code are available at: https://samsunglabs.github.io/RAMP-project-page/

One of the challenging input settings for visual servoing is when the initial and goal camera views are far apart. Such settings are difficult because the wide baseline can cause drastic changes in object appearance and cause occlusions. This paper presents a novel self-supervised visual servoing method for wide baseline images which does not require 3D ground truth supervision. Existing approaches that regress absolute camera pose with respect to an object require 3D ground truth data of the object in the forms of 3D bounding boxes or meshes. We learn a coherent visual representation by leveraging a geometric property called 3D equivariance—the representation is transformed in a predictable way as a function of 3D transformation. To ensure that the feature-space is faithful to the underlying geodesic space, a geodesic preserving constraint is applied in conjunction with the equivariance. We design a Siamese network that can effectively enforce these two geometric properties without requiring 3D supervision. With the learned model, the relative transformation can be inferred simply by following the gradient in the learned space and used as feedback for closed-loop visual servoing. Our method is evaluated on objects from the YCB dataset, showing meaningful outperformance on a visual servoing task, or object alignment task with respect to state-of-the-art approaches that use 3D supervision. Ours yields more than 35% average distance error reduction and more than 90% success rate with 3cm error tolerance.

This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efficiently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function.We compare c2g-HOF with traditional planning algorithms for various robots and planning scenarios. The experimental results indicate that planning with c2g-HOF is significantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. Furthermore, despite being trained from sparsely sampled trajectories in configuration space, c2g-HOF generalizes to generate smoother, and often lower cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0.13 seconds for the entire trajectory.

This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with uniform sampling. To address this challenge, we present an adaptive sampling method which makes use of sampling based planners along with local, closed-form solutions to generate training samples. The cost-to-go function over a specific workspace is represented as a neural network whose weights are generated by a second, higher order network. The networks are trained in an end-to-end fashion. In our previous work, this architecture was shown to successfully learn to generate the cost-to-go functions of holonomic systems using uniform sampling. In this work, we show that uniform sampling fails for non-holonomic systems. However, with the proposed adaptive sampling methodology, our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles. Experiments show that our method is two orders of magnitude faster compared to traditional approaches in cluttered environments.

We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate “expert” training trajectories from a small amount of human-labeled data. In contrast to the traditional sense-plan-act cycle, we propose a deep learning architecture and training regimen called PtPNet that can estimate effective end-effector trajectories for manipulation directly from a single RGB-D image of an object. Additionally, we present a data collection and augmentation pipeline that enables the automatic generation of large numbers (millions) of training image and trajectory examples with almost no human labeling effort.We demonstrate our approach in a non-prehensile tool-based manipulation task, specifically picking up shoes with a hook. In hardware experiments, PtPNet generates motion plans (open-loop trajectories) that reliably (89% success over 189 trials) pick up four very different shoes from a range of positions and orientations, and reliably picks up a shoe it has never seen before. Compared with a traditional sense-plan-act paradigm, our system has the advantages of operating on sparse information (single RGB-D frame), producing high-quality trajectories much faster than the expert planner (300ms versus several seconds), and generalizing effectively to previously unseen shoes. Video available at https://youtu.be/voIkyiBtwn4.

This paper presents a novel constrained, sampling-based motion planning method for grasp and transport tasks with a redundant robotic manipulator. We utilize a planning margin for grasping with constraints that allow the best grasp configuration and approach direction to be determined automatically. For manipulators with many degrees of freedom, our method efficiently chooses the optimal grasp pose when there are many redundant solutions. The method also introduces a parameterized intermediate pose that is optimized to determine the approach direction, increasing robustness under sensor uncertainty and execution errors. Our method also considers transporting the grasped object to the desired target position using a Rapidly-exploring Random Tree (RRT) algorithm that incorporates soft constraints via appropriate cost penalties. We demonstrate the effectiveness and efficiency of our algorithms on a number of simulated and experimental applications. Our experimental results show a marked improvement in computational efficiency in comparison to previously studied approaches.

Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficient to not uniformly explore the state space, especially when there is prior information about its structure. Previous methods have attempted to modify the sampling distribution using hand selected heuristics that can work well for specific environments but not universally. In this paper, a policy-search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. It utilizes information from past searches in similar environments to generate better distributions in novel environments, thus reducing overall computational cost. Our method can be incorporated with a variety of sampling-based planners to improve performance. Our approach is validated on a number of tasks, including a 7DOF robot arm, showing marked improvement in number of collision checks as well as number of nodes expanded compared with baseline methods.

This paper presents a novel adaptive approach to fast sampling-based motion planning by learning models of collision and collision-free regions in configuration spaces in an online manner. The proposed approach incrementally learns Gaussian Mixture Models (GMMs) for collision detection in high dimensional configuration spaces. In practical applications for robotic manipulation, the representation of collision and collision-free regions in configuration space can change due to relative motion between the robot base and workspace. We show how to rapidly adapt to such changes by using inverse kinematics to transform the parameters of the Gaussian mixture model to new configurations. The transformed model is initially used as a prior and then continually updated and refined as the RRT planning algorithm proceeds in real-time. This approach is extremely computationally efficient, and our proposed method is compared with traditional sampling-based planning methods on a number of experimental robot arm planning scenarios.

This paper presents a new approach for fast collision detection in high dimensional configuration spaces for Rapidly-exploring Random Trees (RRT) motion planning. The proposed method is based upon Gaussian Mixture Models (GMM) that are learned using an incremental Expectation Maximization clustering algorithm trained online using exemplars provided by a slow, conventional kinematic-based collision detection routine. The number of collision checks needed can be drastically reduced using a biased random sampling from the learned GMM distribution, and the learned models are continually refined and improved as the RRT planning algorithm proceeds. Our proposed method is demonstrated on several example applications and experimental results show marked improvement in computational efficiency over previous approaches.

Major rule changes for the RoboCup Humanoid League in 2015 pose significant vision and locomotion challenges for disambiguating similarly colored objects and navigating soft terrain. These significant changes highlight the need for applying general purpose humanoid robotics approaches that can handle abrupt environment modifications, and we utilize the general purpose THOR (Tactical Hazardous Operations Robot) series of robot from the recent DARPA Robotics Challenge (DRC). Specific techniques for vision, kicking and autonomy complement software developed for robust deployments in the DRC. In this paper, we present these soccer playing techniques, which were validated in the Humanoid AdultSize league in Hefei. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is finished. We also propose a navigation algorithm which uses the topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls and many static obstacles. The proposed algorithm also has an advantage that errors occurred in each node are mutually independent and can be compensated exactly after some navigation using barcode. Simulation and experimental results were performed to verify the algorithm in the barcode environment, and the result showed an excellent performance. After mapping, it is also possible to solve the kidnapped case and generate paths using topological information

A localization algorithm for topological maps with dynamics is proposed in this paper. Especially, this algorithm considers a localization when some nodes are deleted by door closing and has two main features. First, lots of edge information are used in efficient way. Second, the algorithm calculates probability of a node to be the current location in a systematic and general way. Experiments in a topological map with dynamics are conducted to show the performance of the proposed algorithm.