Probabilistic Articulated Real-Time Tracking for Robot Manipulation

Cifuentes, Cristina Garcia; Issac, Jan; Wüthrich, Manuel; Schaal, Stefan; Bohg, Jeannette

doi:10.1109/lra.2016.2645124

Cited by 68 publications

(57 citation statements)

References 15 publications

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“…On the real robot, we use the Kinect v2 camera. In simulation, we use CHAI3D [16] for rendering images and robot meshes for rendering depths [27]. The force sensor provides 6-axis feedback on the forces and moments along the x, y, z axes.…”

Section: Experiments: Design and Setupmentioning

confidence: 99%

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

et al. 2020

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Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines these modalities which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. In this work, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot.

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Section: Experiments: Design and Setupmentioning

confidence: 99%

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

et al. 2020

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“…Dynamic SLAM: Research on tracking and reconstruction of articulated objects such as human body parts [23,24] or robots [18,6] is related to dynamic SLAM. Recently, some RGB-D SLAM methods have been proposed that represent and track moving rigid objects.…”

Section: Related Workmentioning

confidence: 99%

EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association

Strecke

Stueckler

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a novel approach to dynamic SLAM with dense object-level representations. We represent rigid objects in local volumetric signed distance function (SDF) maps, and formulate multi-object tracking as direct alignment of RGB-D images with the SDF representations. Our main novelty is a probabilistic formulation which naturally leads to strategies for data association and occlusion handling. We analyze our approach in experiments and demonstrate that our approach compares favorably with the state-of-the-art methods in terms of robustness and accuracy.

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“…We compute signed distances using libccd 3 based on a combination of the Gilbert-Johnson-Keerthi (GJK) algorithm and the expanding polytope algorithm (EPA), extensive details are found in (Van Den Bergen, 2001). For object pose feedback controller, we use a GPU based particle tracker from Garcia Cifuentes et al (2017) to track the object using a NVIDIA GTX 1060 GPU.…”

Section: Trajectory Generation and Feedback Implementationmentioning

confidence: 99%

Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

Sundaralingam

Hermans

2018

Auton Robot

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This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on 10 objects from the YCB dataset. However, the proposed method is general enough to generate motions for most objects the robot can grasp. Experimental result support the feasibillty of its application across a variety of object shapes. We explore the adaptability of our approach to additional task requirements by including collision avoidance and joint space smoothness costs. The grasped object avoids collisions with the environment by the use of a signed distance cost function. We reduce the effects of unmodeled object dynamics by requiring smooth joint trajectories. We additionally compensate for errors encountered during trajectory execution by formulating an object pose feedback controller.

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Probabilistic Articulated Real-Time Tracking for Robot Manipulation

Cited by 68 publications

References 15 publications

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association

Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

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