Structured manifolds for motion production and segmentation : a structured Kernel Regression approach

Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010

Bio-Inspired Motion Strategies for a Bimanual Manipulation Task

Steffen JF, Elbrechter C, Haschke R, Ritter H. Bio-Inspired Motion Strategies for a Bimanual Manipulation Task. In: International Conference on Humanoid Robots (Humanoids). 2010

Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation

In this paper, we present a novel method for achieving dexterous manipulation of complex objects, while simultaneously securing the object without the use of passive support surfaces. We posit that a key difficulty for training such policies in a Reinforcement Learning framework is the difficulty of exploring the problem state space, as the accessible regions of this space form a complex structure along manifolds of a high-dimensional space. To address this challenge, we use two versions of the non-holonomic Rapidly-Exploring Random Trees algorithm; one version is more general, but requires explicit use of the environment's transition function, while the second version uses manipulation-specific kinematic constraints to attain better sample efficiency. In both cases, we use states found via sampling-based exploration to generate reset distributions that enable training control policies under full dynamic constraints via model-free Reinforcement Learning. We show that these policies are effective at manipulation problems of higher difficulty than previously shown, and also transfer effectively to real robots. Videos of the real-hand demonstrations can be found on the project website: https://sbrl.cs.columbia.edu/Comment: 10 pages, 6 figures, submitted to Robotics Science & Systems 202

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Learning a State Estimator for Tactile In-Hand Manipulation

We study the problem of estimating the pose of an object which is being manipulated by a multi-fingered robotic hand by only using proprioceptive feedback. To address this challenging problem, we propose a novel variant of differentiable particle filters, which combines two key extensions. First, our learned proposal distribution incorporates recent measurements in a way that mitigates weight degeneracy. Second, the particle update works on non-euclidean manifolds like Lie-groups, enabling learning-based pose estimation in 3D on SE(3). We show that the method can represent the rich and often multi-modal distributions over poses that arise in tactile state estimation. The models are trained in simulation, but by using domain randomization, we obtain state estimators that can be employed for pose estimation on a real robotic hand (equipped with joint torque sensors). Moreover, the estimator runs fast, allowing for online usage with update rates of more than 100 Hz on a single CPU core. We quantitatively evaluate our method and benchmark it against other approaches in simulation. We also show qualitative experiments on the real torque-controlled DLR-Hand II

A friendly teaching system for dexterous manipulation tasks of multi-fingered hands.

by Lam Pak Chio.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 101-105).Abstract also in Chinese.Abstract --- p.iiAcknowledgements --- p.vContentsChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Problem Definition and Approach --- p.3Chapter 1.3 --- Outline --- p.5Chapter 2 --- Algorithm Outline --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Assumptions --- p.7Chapter 2.3 --- Object Model --- p.8Chapter 2.4 --- Hand Model --- p.9Chapter 2.5 --- Measurement Data --- p.11Chapter 2.6 --- Algorithm Outline --- p.12Chapter 3 --- Calculation of Contact States --- p.14Chapter 3.1 --- Introduction --- p.14Chapter 3.2 --- Problem Analysis --- p.15Chapter 3.3 --- Details of Algorithm --- p.17Chapter 3.3.1 --- Calculation of Contact Points --- p.18Chapter 3.3.2 --- Calculation of Object Position and Orientation --- p.26Chapter 3.3.2.1 --- The Object Orientation --- p.26Chapter 3.3.2.2 --- The Object Position --- p.28Chapter 3.3.3 --- Contact Points on Other Fingers --- p.32Chapter 4 --- Calculation of Contact Motion --- p.34Chapter 4.1 --- Introduction --- p.34Chapter 4.2 --- Search-tree --- p.34Chapter 4.3 --- Cost Function --- p.36Chapter 4.4 --- Details of Algorithm --- p.37Chapter 4.4.1 --- Calculation of the Next Instant Contact States --- p.39Chapter 4.4.1.1 --- Contact Region Estimation --- p.41Chapter 4.4.1.2 --- Contact Point Calculation --- p.45Chapter 4.4.1.3 --- Object Position and Orientation Calculation --- p.48Chapter 4.4.1.4 --- Contact Motion Calculation --- p.50Chapter 5 --- Implementation --- p.56Chapter 5.1 --- Introduction --- p.56Chapter 5.2 --- Architecture of Friendly Teaching System --- p.56Chapter 5.2.1 --- CyberGlove --- p.57Chapter 5.2.2 --- CyberGlove Interface Unit --- p.57Chapter 5.2.3 --- Host Computer --- p.58Chapter 5.2.4 --- Software --- p.58Chapter 5.3 --- Algorithm Implementation --- p.59Chapter 5.4 --- Examples for Calculation of Contact Configuration --- p.59Chapter 5.5 --- Simulation --- p.68Chapter 5.6 --- Experiments --- p.82Chapter 5.6.1 --- Translation of an Object --- p.82Chapter 5.6.2 --- Rotation of an Object --- p.90Chapter 6 --- Conclusions --- p.98References --- p.101Appendix --- p.10

TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement

We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. Existing hand trackers, especially those that rely on very few cameras, often produce visually unrealistic results with hand-object intersection or missing contacts. Although correcting such errors requires reasoning about temporal aspects of interaction, most previous works focus on static grasps and contacts. The core of our method are TOCH fields, a novel spatio-temporal representation for modeling correspondences between hands and objects during interaction. TOCH fields are a point-wise, object-centric representation, which encode the hand position relative to the object. Leveraging this novel representation, we learn a latent manifold of plausible TOCH fields with a temporal denoising auto-encoder. Experiments demonstrate that TOCH outperforms state-of-the-art 3D hand-object interaction models, which are limited to static grasps and contacts. More importantly, our method produces smooth interactions even before and after contact. Using a single trained TOCH model, we quantitatively and qualitatively demonstrate its usefulness for correcting erroneous sequences from off-the-shelf RGB/RGB-D hand-object reconstruction methods and transferring grasps across objects