Autonomously Acquiring Instance-Based Object Models from Experience

25 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “For example, Dex-Net used a dataset of 10,000 3D models, combined with a learning framework to acquire force closure grasps (Mahler et al., 2016), while the work of Oberlin & Tellex (2015) proposed autonomously collecting object scans using a Baxter robot.”
Self-Supervised Visual Planning with Temporal Skip Connections (2017, background): “Oberlin and Tellex [17] proposed to collect object scans using multiple robots.”
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017, methodology): “The time cost of generating samples on a physical robot led to the development active methods for acquiring grasping experiences such as Multi-Armed Bandits using Correlated Beta Processes [37] or Prior Confidence Bounds [39].”
Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards (2016, methodology): “We plan to study other quality metrics such as success on physical trials [25], [31] and alternate MAB methods based on upper confidence bounds [25], [32] or Gittins indices [26] in future work.”