Search Results for author: Ken Goldberg

Found 76 papers, 19 papers with code

LERF: Language Embedded Radiance Fields

no code implementations16 Mar 2023 Justin Kerr, Chung Min Kim, Ken Goldberg, Angjoo Kanazawa, Matthew Tancik

Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances.

Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations

no code implementations14 Oct 2022 Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg

Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify sparse reward functions.

Continuous Control

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

no code implementations13 Oct 2022 Wisdom C. Agboh, Satvik Sharma, Kishore Srinivas, Mallika Parulekar, Gaurav Datta, Tianshuang Qiu, Jeffrey Ichnowski, Eugen Solowjow, Mehmet Dogar, Ken Goldberg

In physical experiments, we find a 13. 7% increase in success rate, a 1. 6x increase in picks per hour, and a 6. 3x decrease in grasp planning time compared to prior work on multi-object grasping.


SpeedFolding: Learning Efficient Bimanual Folding of Garments

1 code implementation22 Aug 2022 Yahav Avigal, Lars Berscheid, Tamim Asfour, Torsten Kröger, Ken Goldberg

Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments.

Automated Pruning of Polyculture Plants

no code implementations22 Aug 2022 Mark Presten, Rishi Parikh, Shrey Aeron, Sandeep Mukherjee, Simeon Adebola, Satvik Sharma, Mark Theis, Walter Teitelbaum, Ken Goldberg

Using an overhead camera to collect data from a physical scale garden testbed, the autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day.

Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision

1 code implementation29 Jun 2022 Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg

With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time.

Continual Learning

DayDreamer: World Models for Physical Robot Learning

1 code implementation28 Jun 2022 Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel

Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.

Navigate reinforcement-learning +1

Multi-Object Grasping in the Plane

no code implementations1 Jun 2022 Wisdom C. Agboh, Jeffrey Ichnowski, Ken Goldberg, Mehmet R. Dogar

In physical grasping experiments comparing performance with a single-object picking baseline, we find that the frictionless multi-object grasping system achieves 13. 6\% higher grasp success and is 59. 9\% faster, from 212 PPH to 340 PPH.

Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research

no code implementations21 Apr 2022 Ryan Hoque, Kaushik Shivakumar, Shrey Aeron, Gabriel Deza, Aditya Ganapathi, Adrian Wong, Johnny Lee, Andy Zeng, Vincent Vanhoucke, Ken Goldberg

Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware.

Benchmarking Imitation Learning

Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions

no code implementations28 Mar 2022 Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, Pieter Abbeel

We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.

Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models

no code implementations8 Mar 2022 Vincent Lim, Ellen Novoseller, Jeffrey Ichnowski, Huang Huang, Ken Goldberg

For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards.

Experimental Design reinforcement-learning +1

AlphaGarden: Learning to Autonomously Tend a Polyculture Garden

1 code implementation11 Nov 2021 Mark Presten, Yahav Avigal, Mark Theis, Satvik Sharma, Rishi Parikh, Shrey Aeron, Sandeep Mukherjee, Sebastian Oehme, Simeon Adebola, Walter Teitelbaum, Varun Kamat, Ken Goldberg

This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1. 5m x 3. 0m physical testbed.

Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects

1 code implementation27 Oct 2021 Jeffrey Ichnowski, Yahav Avigal, Justin Kerr, Ken Goldberg

The ability to grasp and manipulate transparent objects is a major challenge for robots.

Transparent objects

ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning

no code implementations17 Sep 2021 Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller, Albert Wilcox, Daniel S. Brown, Ken Goldberg

Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation learning: is it possible to control the timing and length of interventions to both facilitate learning and limit burden on the human supervisor?

Imitation Learning

Accelerating Quadratic Optimization with Reinforcement Learning

1 code implementation NeurIPS 2021 Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg

First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.

reinforcement-learning Reinforcement Learning (RL)

Policy Gradient Bayesian Robust Optimization for Imitation Learning

no code implementations11 Jun 2021 Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg

Results suggest that PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse and outperforms state-of-the-art imitation learning algorithms when learning from ambiguous demonstrations by hedging against uncertainty, rather than seeking to uniquely identify the demonstrator's reward function.

Imitation Learning

PAC Best Arm Identification Under a Deadline

no code implementations6 Jun 2021 Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez

In this work, the decision-maker is given a deadline of $T$ rounds, where, on each round, it can adaptively choose which arms to pull and how many times to pull them; this distinguishes the number of decisions made (i. e., time or number of rounds) from the number of samples acquired (cost).

Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms

no code implementations29 May 2021 Shivin Devgon, Jeffrey Ichnowski, Ashwin Balakrishna, Harry Zhang, Ken Goldberg

We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images.

Self-Supervised Learning

LazyDAgger: Reducing Context Switching in Interactive Imitation Learning

no code implementations31 Mar 2021 Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S. Brown, Daniel Seita, Brijen Thananjeyan, Ellen Novoseller, Ken Goldberg

Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior.

Continuous Control Imitation Learning

VisuoSpatial Foresight for Physical Sequential Fabric Manipulation

no code implementations19 Feb 2021 Ryan Hoque, Daniel Seita, Ashwin Balakrishna, Aditya Ganapathi, Ajay Kumar Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg

We build upon the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different sequential fabric manipulation tasks with a single goal-conditioned policy.

Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search

no code implementations7 Dec 2020 Andrey Kurenkov, Roberto Martín-Martín, Jeff Ichnowski, Ken Goldberg, Silvio Savarese

We propose to use a 3D scene graph representation to capture the hierarchical, semantic, and geometric aspects of this problem.

Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop

no code implementations7 Dec 2020 Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, Melissa Mozifian, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White

This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.

Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

no code implementations6 Dec 2020 Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, Andy Zeng

Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag".

Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects

no code implementations11 Nov 2020 Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Shivin Devgon, Ken Goldberg

However, these policies can consistently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps.

Accelerating Grasp Exploration by Leveraging Learned Priors

no code implementations11 Nov 2020 Han Yu Li, Michael Danielczuk, Ashwin Balakrishna, Vishal Satish, Ken Goldberg

The ability of robots to grasp novel objects has industry applications in e-commerce order fulfillment and home service.

Thompson Sampling

Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables

no code implementations10 Nov 2020 Harry Zhang, Jeffrey Ichnowski, Daniel Seita, Jonathan Wang, Huang Huang, Ken Goldberg

The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations.

Self-Supervised Learning

Untangling Dense Knots by Learning Task-Relevant Keypoints

no code implementations10 Nov 2020 Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Jeffrey Ichnowski, Ashwin Balakrishna, Minho Hwang, Vainavi Viswanath, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg

HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97. 9% of 378 simulation experiments with an average of 12. 1 actions per trial.

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

2 code implementations29 Oct 2020 Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

reinforcement-learning Reinforcement Learning (RL) +1

Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking

no code implementations20 Jul 2020 Kate Sanders, Michael Danielczuk, Jeffrey Mahler, Ajay Tanwani, Ken Goldberg

A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

no code implementations31 May 2020 Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan, Raghav Anand, Mariano Phielipp, Ken Goldberg

We demonstrate the use of this representation to imitate surgical suturing motions from publicly available videos of the JIGSAWS dataset.

Action Segmentation Metric Learning +1

X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions

no code implementations20 Apr 2020 Michael Danielczuk, Anelia Angelova, Vincent Vanhoucke, Ken Goldberg

For applications in e-commerce, warehouses, healthcare, and home service, robots are often required to search through heaps of objects to grasp a specific target object.

Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks

no code implementations19 Mar 2020 Minho Hwang, Brijen Thananjeyan, Samuel Paradis, Daniel Seita, Jeffrey Ichnowski, Danyal Fer, Thomas Low, Ken Goldberg

Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis.

GOMP: Grasp-Optimized Motion Planning for Bin Picking

no code implementations5 Mar 2020 Jeffrey Ichnowski, Michael Danielczuk, Jingyi Xu, Vishal Satish, Ken Goldberg

Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH).


ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions

no code implementations3 Mar 2020 Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Joseph E. Gonzalez, Aaron Ames, Ken Goldberg

Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks.

Continuous Control

Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot

no code implementations15 Feb 2020 Minho Hwang, Daniel Seita, Brijen Thananjeyan, Jeffrey Ichnowski, Samuel Paradis, Danyal Fer, Thomas Low, Ken Goldberg

We report experimental results for a handover-free version of the peg transfer task, performing 20 and 5 physical episodes with single- and bilateral-arm setups, respectively.


Continuous Online Learning and New Insights to Online Imitation Learning

no code implementations3 Dec 2019 Jonathan Lee, Ching-An Cheng, Ken Goldberg, Byron Boots

We prove that there is a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs, and we present a reduction from dynamic regret to both static regret and convergence rate of the associated EP.

Imitation Learning

Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor

1 code implementation23 Sep 2019 Daniel Seita, Aditya Ganapathi, Ryan Hoque, Minho Hwang, Edward Cen, Ajay Kumar Tanwani, Ashwin Balakrishna, Brijen Thananjeyan, Jeffrey Ichnowski, Nawid Jamali, Katsu Yamane, Soshi Iba, John Canny, Ken Goldberg

In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone.

Imitation Learning

Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

no code implementations31 May 2019 Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan Mcallister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg

Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.

Model-based Reinforcement Learning reinforcement-learning +1

Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

no code implementations4 Mar 2019 Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna, Matthew Matl, David Wang, Roberto Martín-Martín, Animesh Garg, Silvio Savarese, Ken Goldberg

In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin.


Online Learning with Continuous Variations: Dynamic Regret and Reductions

no code implementations19 Feb 2019 Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots

Furthermore, we show for COL a reduction from dynamic regret to both static regret and convergence in the associated EP, allowing us to analyze the dynamic regret of many existing algorithms.

Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning

1 code implementation6 Nov 2018 Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg

In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality.

Imitation Learning

Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data

2 code implementations16 Sep 2018 Michael Danielczuk, Matthew Matl, Saurabh Gupta, Andrew Li, Andrew Lee, Jeffrey Mahler, Ken Goldberg

We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.

Object Tracking Semantic Segmentation +1

A Fog Robotic System for Dynamic Visual Servoing

no code implementations16 Sep 2018 Nan Tian, Jinfa Chen, Mas Ma, Robert Zhang, Bill Huang, Ken Goldberg, Somayeh Sojoudi

We use the system to enable robust teleoperation of a dynamic self-balancing robot from the cloud.

Object Recognition

Learning to Optimize Join Queries With Deep Reinforcement Learning

no code implementations9 Aug 2018 Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph Hellerstein, Ion Stoica

Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space.


Parametrized Hierarchical Procedures for Neural Programming

no code implementations ICLR 2018 Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica

Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.

Imitation Learning

RLlib: Abstractions for Distributed Reinforcement Learning

3 code implementations ICML 2018 Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael. I. Jordan, Ion Stoica

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.

reinforcement-learning Reinforcement Learning (RL)

A Berkeley View of Systems Challenges for AI

no code implementations15 Dec 2017 Ion Stoica, Dawn Song, Raluca Ada Popa, David Patterson, Michael W. Mahoney, Randy Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David Culler, Pieter Abbeel

With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.

Machine Translation speech-recognition +1

Learning Robust Bed Making using Deep Imitation Learning with DART

no code implementations7 Nov 2017 Michael Laskey, Chris Powers, Ruta Joshi, Arshan Poursohi, Ken Goldberg

Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions.


Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning

no code implementations4 Nov 2017 Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg

We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise.

Autonomous Driving reinforcement-learning +1

Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure

1 code implementation19 Sep 2017 Daniel Seita, Sanjay Krishnan, Roy Fox, Stephen McKinley, John Canny, Ken Goldberg

In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet.


Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning

no code implementations19 Sep 2017 Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David Gealy, Ken Goldberg

Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact.


DART: Noise Injection for Robust Imitation Learning

2 code implementations27 Mar 2017 Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg

One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.

Imitation Learning

Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

no code implementations27 Mar 2017 Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, Ken Goldberg

To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.


Multi-Level Discovery of Deep Options

no code implementations24 Mar 2017 Roy Fox, Sanjay Krishnan, Ion Stoica, Ken Goldberg

Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces.

Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations

no code implementations4 Oct 2016 Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg

Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.

ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models

no code implementations15 Jan 2016 Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, Ken Goldberg

Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of-date, or outlier data.

Active Learning Electroencephalogram (EEG) +1

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