Neighborhood denoising for learning high-dimensional grasping manifolds

Tsoli, Aggeliki; Jenkins, Odest Chadwicke

doi:10.1109/iros.2008.4651228

Cited by 11 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the optimization does not distinguish between approach and grasping phase; therefore the optimized grasp pose might be based only on approaching poses. In [8] data from a Vicon optical motion capture system is used to create a latent space for "Interactive Control of a Robot Hand" using Isomap. The data is a concatenation of different grasps and tapping demonstrations.…”

Section: Contributions and Related Workmentioning

confidence: 99%

“…The inverse width parameter was set to 0.001 by inspection. Following [6], [8], we selected a dimensionality of 2 for the latent space, simplifying the visualization of the results. Although higher dimensional latent spaces could improve the separability of grasps, the advantages of GPLVM over other dimensionality reduction techniques can be shown already in 2D.…”

Section: A Low Dimensional Representation Of the Grasp Movementsmentioning

confidence: 99%

“…Furthermore, the low-dimensional space was created with PCA, which is limited by its linear nature. The benefits of non-linear dimensionality reduction schemes are shown in [8], where a 2D space is used for the control of robotic grasps. In a similar way, we compare the 2D latent space generated by our model and other dimensionality reduction techniques such as Principal Component Analysis (PCA), Isomap or Locally Linear Embedding (LLE), showing the advantages of our approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-temporal modeling of grasping actions

Romero¹,

Feix

Kragić³

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Understanding the spatial dimensionality and temporal context of human hand actions can provide representations for programming grasping actions in robots and inspire design of new robotic and prosthetic hands. The natural representation of human hand motion has high dimensionality. For specific activities such as handling and grasping of objects, the commonly observed hand motions lie on a lower-dimensional non-linear manifold in hand posture space. Although full body human motion is well studied within Computer Vision and Biomechanics, there is very little work on the analysis of hand motion with nonlinear dimensionality reduction techniques. In this paper we use Gaussian Process Latent Variable Models (GPLVMs) to model the lower dimensional manifold of human hand motions during object grasping. We show how the technique can be used to embed high-dimensional grasping actions in a lower-dimensional space suitable for modeling, recognition and mapping.

show abstract

Section: Contributions and Related Workmentioning

confidence: 99%

Section: A Low Dimensional Representation Of the Grasp Movementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatio-temporal modeling of grasping actions

Romero¹,

Feix

Kragić³

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…Conceptually, this representation of the collection of demonstrations is an embedding in two dimensions: time and variation between demonstrations. Experiments conducted during our own early work, as well as by other dimensionality-reduction researchers [67], indicated that two dimensions were usually sufficient to represent manipulation tasks occurring in six to eight dimensions.…”

Section: Trajectory Embeddingmentioning

confidence: 78%

“…These algorithms are robust to even a large number of false negatives, that is, neighbor links missing where they should be present, but even a single false positive link can have disastrous effects on the embackground: dimensionality reduction bedding [67]. Spurious neighbor links create "short circuit" connections between unrelated portions of trajectories, thus lowering the geodesic distances between points in these semantically distant regions.…”

Section: Inherent Difficultiesmentioning

confidence: 99%

Graph-based trajectory planning through programming by demonstration

Melchior

Simmons

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Autonomous robots are becoming increasingly commonplace in industry, space exploration, and even domestic applications. These diverse fields share the need for robots to perform increasingly complex motion behaviors for interacting with the world. As the robots' tasks become more varied and sophisticated, though, the challenge of programming them becomes more difficult and domain-specific. Robotics experts without domain knowledge may not be well-suited for communicating taskspecific goals and constraints to the robot, but domain experts may not possess the skills for programming robots through conventional means. Ideally, any person capable of demonstrating the necessary skill should be able to instruct the robot to do so. In this thesis, we examine the use of demonstration to program or, more aptly, to teach a robot to perform precise motion tasks.Programming by Demonstration (PbD) offers an expressive means for teaching while being accessible to domain experts who may be novices in robotics. This learning paradigm relies on human demonstrations to build a model of a motion task. This thesis develops an algorithm for learning from examples that is capable of producing provably collisionfree trajectories without requiring special training for the teacher or a model of the environment. This approach is capable of learning precise motions, even when the precision required is on the same order of magnitude as the noise in the demonstrations. Finally, this approach is robust to the occasional errors in strategy and jitter in movement inherent in imperfect human demonstrations.The approach contributed in this thesis begins with the construction of a neighbor graph, which determines the correspondences between multiple imperfect demonstrations. This graph permits the robot to plan novel trajectories that safely and smoothly generalize the teacher's behavior. Finally, like any good learner, a robot should assess its knowledge and ask questions about any detected deficiencies. The learner presented here detects regions of the task in which the demonstrations appear to be ambiguous or insufficient, and requests additional information from the teacher. This algorithm is demonstrated in example domains with a 7 degree-of-freedom manipulator, and user trials are presented.

show abstract

Learning Kinematic Models of Articulated Objects

Sturm

Pradeep

Stachniss

et al. 2013

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

Topic: estimation, prediction Oral presentation or poster presentationHome environments are envisioned as one of the key application areas for service robots. Robots operating in such environments are typically faced with a variety objects they have to deal with or to manipulate to fulfill a given task. Many objects are not rigid since they have moving parts such as drawers or doors. Understanding the spatial movements of parts of such objects is essential for service robots to allow them to plan relevant actions such as door-opening trajectories. Ideally, robots are able to autonomously infer these articulation models by observation. In this work, we therefore investigate the problem of learning kinematic models of articulated objects from observations. As an illustrating example, consider the left three images of Figure 1 which depict two examples for observations of the door of a microwave oven and a learned, one-dimensional description of the door motion.Our problem can be formulated as follows: Given a sequence of rigid body poses from observed objects parts, learn a compact kinematic model describing the whole articulated object. This kinematic model has to define (i) which parts are connected, (ii) the dimensionality of the latent (not observed) actuation space of the object, and (iii) a kinematic function between different body parts in a generative way allowing a robot to reason also about unseen configurations. Our approach is related to recent work of Katz et al. [1] who learn planar kinematic models for articulated objects such as scissors by manipulating the object as well as to the work of Yan and Pollefeys [4] who present an approach for learning the structure of an articulated object from feature trajectories under affine projections.The contribution of this work is a novel approach for learning actuation models based on observations only. Our method is able to robustly detect the connectivity of the rigid parts of the object and to estimate accurate articulation models from a candidate set. Our approach allows for selecting the best model among parametric, expert-designed transformation templates (rotational and prismatic models), and nonparametric transformations that are learned from scratch requiring minimal prior assumptions. To obtain a parameter-free description, we apply Gaussian processes [2] as a non-parametric regression technique to learn flexible and accurate models. To find the low-dimensional description of the moving parts, we furthermore apply local linear embedding [3], which is a non-linear dimensionality reduction technique.We implemented our approach on a real robot and tested it by estimating models of different objects, including a door of a microwave oven (see Figure 1 left), a cabinet with drawers (see Figure 1 right), a garage door (see Figure 2), and a table moved on the ground plane (see Figure 3). Our technique allows to learn accurate models for different articulated objects. We regard this as an important step towards autonomous robots understanding and actively ha...

show abstract

Neighborhood denoising for learning high-dimensional grasping manifolds

Cited by 11 publications

References 17 publications

Spatio-temporal modeling of grasping actions

Spatio-temporal modeling of grasping actions

Graph-based trajectory planning through programming by demonstration

Learning Kinematic Models of Articulated Objects

Contact Info

Product

Resources

About