We present a fully autonomous robotic system for grasping objects in dense clutter. The are unknown and have arbitrary shapes. Therefore, we cannot rely on prior models. Instead, the robot learns online, from scratch, to manipulate by trial error. Grasping clutter is significantly harder than isolated objects, because needs push move around order create sufficient space fingers. These pre-grasping actions do not an immediate utility, may result unnecessary delays. utility of action can be...

Progress has been achieved recently in object detection given advancements deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability robotics, where solutions must scale number objects variety conditions. work proposes an autonomous process for Convolutional Neural Network (CNN) pose estimation robotic setups. The focus is on detecting placed cluttered, tight environments, as shelf...

We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex interactions that ensue as robot end-effector pushes multiple objects, whose physical properties, including size, shape, mass, and friction coefficients may be unknown priori. DIPN "imagines" the effect of push action generates an accurate synthetic image predicted outcome. is shown sample efficient when trained in simulation or with real robotic system. The high accuracy allows direct integration grasp...

Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots warehouse automation. A variety such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles carefully arranging them bins containers. Desirable solutions need to be low-cost, easily deployable controllable, making minimalistic choices desirable. The challenge designing an effective solution this problem...

Recent progress in robotic manipulation has dealt with the case of previously unknown objects context relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, deliberate placement a tight region, depend critically on shape information to achieve safe execution. This work deals pick-and-constrained without access geometric models. The objective is pick an object and place it safely inside desired goal region any collisions, while minimizing time...

Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can achieved by traditional perception systems.This paper demonstrates that the combination state-of-the-art object tracking passively adaptive mechanical hardware leveraged complete precision tight, industrially-relevant tolerances (0.25mm).The proposed control method closes loop through vision...

We propose a language-driven navigation approach for commanding mobile robots in outdoor environments. consider unknown environments that contain previously unseen objects. The proposed aims at making interactions human-robot teams natural. Robots receive from human teammates commands natural language, such as "Navigate around the building to car left of fire hydrant and near tree". A robot needs first classify its surrounding objects into categories, using images obtained sensors. result...

Active learning typically aims at minimizing the number of labeled samples to be included in training set reach a certain level classification accuracy. Standard methods do not usually take into account real annotation procedures and implicitly assume that all require same effort labeled. Here, we consider case where cost associated with given sample depends on previously samples. In general, this is when annotating queried an action changes state dynamic system, function system. order...

This paper presents a method for identifying mechanical parameters of robots or objects, such as their mass and friction coefficients. Key features are the use off-the-shelf physics engines adaptation Bayesian optimization technique towards minimizing number real-world experiments needed model-based reinforcement learning. The proposed framework reproduces in engine performed on real robot optimizes model's so to match trajectories. optimized model is then used learning policy simulation,...

This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially cases involving occlusions and objects resting on each other. The initial set candidate poses is generated from state-of-the-art detection global point cloud registration techniques. best scored per by using these techniques may not be accurate due to overlaps occlusions. Nevertheless, experimental indications provided this show that with lower...

This letter considers the problem of retrieving an object from many tightly packed objects using a combination robotic pushing and grasping actions. Object retrieval in dense clutter is important skill for robots to operate households everyday environments effectively. The proposed solution, Visual Foresight Tree ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VFT</small> ), intelligently rearranges surrounding target so that it can be grasped...

Fully actuated multifingered robotic hands are often expensive and fragile. Low-cost underactuated appealing but present challenges due to the lack of analytical models. This letter aims learn a stochastic version such models automatically from data with minimum user effort. The focus is on identifying dominant, sensible features required express hand state transitions given quasi-static motions, thereby enabling learning probabilistic transition model recorded trajectories. Experiments both...

Learning to grasp novel objects is an essential skill for robots operating in unstructured environments. We therefore propose a probabilistic approach learning grasp. In particular, we learn function that predicts the success probability of grasps performed on surface points given object. Our based Markov Random Fields (MRF), and motivated by fact are geometrically close each other tend have similar probabilities. The MRF successfully tested simulation, real robot using 3-D scans various...

Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, communicate with human teammates clear natural way. address these challenges, we have developed intelligence architecture that combines cognitive components carry out high-level tasks, semantic perception label regions world, language component command...

We propose a new technique for pushing an unknown object from initial configuration to goal with stability constraints.The proposed method leverages recent progress in differentiable physics models learn mechanical properties of pushed objects, such as their distributions mass and coefficients friction.The learning computes the gradient distance between predicted poses objects actual observed poses, utilizes that search values reduce reality gap.The approach is also utilized optimize policy...

Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against object's 3D model. Despite progress due to CNNs, semantic segmentation output can be noisy, especially when CNN only trained on synthetic data. This causes registration methods fail in estimating a good object pose. work proposes novel stochastic optimization process that treats of CNNs as confidence probability. The algorithm,...

This paper focuses on vision-based pose estimation for multiple rigid objects placed in clutter, especially cases involving occlusions and resting each other. Progress has been achieved recently object recognition given advancements deep learning. Nevertheless, such tools typically require a large amount of training data significant manual effort to label objects. limits their applicability robotics, where solutions must scale number variety conditions. Moreover, the combinatorial nature...