A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms.

http://roboticsproceedings.org/rss09/p12.pdf

Deep learning for detecting robotic grasps

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

/pdf/data-driven-grasp-synthesis-a-survey-3ahemc6ci5.pdf

Data-Driven Grasp Synthesis—A Survey

Deep Learning for Detecting Robotic Grasps

https://hal.archives-ouvertes.fr/hal-00731127/document

An overview of 3D object grasp synthesis algorithms

In this paper, a comanipulation system aimed at providing guidance to bone milling for orthopedic surgery is presented. Contrarily to most existing systems, the proposed approach is able of accounting for eventual movements of the bone with respect to the robot reference base body during the procedure. This property raises from two key components. Firstly, a 3D sensor composed of a camera and a laser projector is mounted on the robot structure in such a way that the milling region can be permanently observed under optimal sensing conditions with a limited risk of occlusions. Secondly, a guide generator running at high frequency interpolates the low frequency data obtained from the sensor in order to generate guiding forces. The paper first provides a presentation of the overall design, with details on the sensing system and the control law. Then, preliminary experiments involving a simplified task emphasize the ability of the system to increase the precision of a subject who is asked to follow a moving environment.

A comanipulation device for orthopedic surgery that generates geometrical constraints with real-time registration on moving bones

This paper presents a new needle path planning method for digital breast tomosynthesis biopsy. Needle insertion planning into deformable tissue for breast biopsy procedure is a challenging task because of the infinite possibilities of insertion points. In addition, the lesion moves from its original position when the radiologist introduces the biopsy needle. The proposed approach couples Rapidly-exploring Random Trees with Finite Element Simulation in order to find an optimal path taking breast deformations into account. Simulation results show that this method reduces the error (i.e. the distance between the needle tip and the lesion) by 80 %.

Needle path planning for digital breast tomosynthesis biopsy

In this paper, we propose a novel idea to address the problem of fast computation of stable force-closure grasp configurations for a multifingered hand and a 3-D rigid object represented as a polygonal soup model. The proposed method performs a low-level shape exploration by wrapping multiple cords around the object in order to quickly isolate promising grasping regions. Around these regions, we compute grasp configurations by applying a variant of the close-until-contact procedure to find the contact points. The finger kinematics and the contact information are then used to filter out unstable grasps. Through many simulated examples with three different anthropomorphic hands, we demonstrate that, compared with previous grasp planners such as the generic grasp planner in Simox, the proposed grasp planner can synthesize grasps that are more natural-looking for humans (as measured by the grasp quality measure skewness) for objects with complex geometries in a short amount of time. Unlike many other planners, this is achieved without costly model preprocessing such as segmentation by parts and medial axis extraction.

https://hal.inria.fr/hal-01250418/document

Fast Grasp Planning Using Cord Geometry

This survey reviews computational algo- rithms for generating 3D objects grasps with autonomous multi-fingered robotic hands. Over the past 20 years, grasping has been an increasingly active research area. Ex- isting papers focus on reviewing the mechanics of grasping and the finger-object contacts interactions (21) or robot hand design and their control (4). Robot grasp synthesis algorithms has been reviewed in (17), but since then an im- portant progress has been made toward applying learning techniques to the grasping problem. This survey focuses on analytical as well as empirical grasp synthesis approaches.

3D Objects Grasps Synthesis: A Survey

Robotic exoskeletons can apply forces distributed on the limbs of the subject they are connected to. This offers a great potential in the field of neurorehabilitation, to address the impairment of interjoint coordination in hemiparetic stroke patients. In these patients, the normal flexible joint rotation synergies are replaced by pathological fixed patterns of rotation. In this paper, we investigate how the concept of synergy can be exploited in the control of an upper limb exoskeleton. The long term goal is to develop a device capable of changing the joint synchronization of a patient performing exercises during rehabilitation.

/pdf/changing-human-upper-limb-synergies-with-an-exoskeleton-3t6mhvitgc.pdf

Anis Sahbani

Papers

A comanipulation device for orthopedic surgery that generates geometrical constraints with real-time registration on moving bones

Needle path planning for digital breast tomosynthesis biopsy

Fast Grasp Planning Using Cord Geometry

3D Objects Grasps Synthesis: A Survey

Changing human upper-limb synergies with an exoskeleton using viscous fields