• Publications
  • Influence
Eigentaste: A Constant Time Collaborative Filtering Algorithm
TLDR
This work compares Eigentaste to alternative algorithms using data from Jester, an online joke recommending system, and uses the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms.
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
TLDR
Experiments with over 1,000 trials on an ABB YuMi comparing grasp planning methods on singulated objects suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be used to plan grasps in 0.8sec with a success rate of 93% on eight known objects with adversarial geometry.
Motion planning with sequential convex optimization and convex collision checking
TLDR
A sequential convex optimization procedure, which penalizes collisions with a hinge loss and increases the penalty coefficients in an outer loop as necessary, and an efficient formulation of the no-collisions constraint that directly considers continuous-time safety are presented.
RLlib: Abstractions for Distributed Reinforcement Learning
TLDR
This work argues for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks, through RLlib: a library that provides scalable software primitives for RL.
A Survey of Research on Cloud Robotics and Automation
TLDR
This survey considers robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system.
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
In this paper we present LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will be used during
Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping
TLDR
An information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner is proposed and reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57%Compared to an information-based strategy that uses global planning.
Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards
TLDR
The Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning, and reports on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction.
Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation
TLDR
A single-camera statistical segmentation and tracking algorithm that combines statistical background image estimation, per-pixel Bayesian segmentation, and an approximate solution to the multi-target tracking problem using a bank of Kalman filters and Gale-Shapley matching is introduced.
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
TLDR
It is described how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks and how imitation learning can learn deep neural network policies that can acquire the demonstrated skills.
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