Search Results for author: Daniel Seita

Found 18 papers, 5 papers with code

DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning

no code implementations15 Sep 2021 Daniel Seita, Abhinav Gopal, Zhao Mandi, John Canny

Then, students learn by running either offline RL or by using teacher data in combination with a small amount of self-generated data.

Offline RL reinforcement-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.

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".

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

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.

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.

Robotics

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

2 code implementations26 Oct 2019 Daniel Seita, David Chan, Roshan Rao, Chen Tang, Mandi Zhao, John Canny

Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning.

Atari Games Q-Learning +1

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

Risk Averse Robust Adversarial Reinforcement Learning

no code implementations31 Mar 2019 Xinlei Pan, Daniel Seita, Yang Gao, John Canny

In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary.

reinforcement-learning

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.

Robotics

An Efficient Minibatch Acceptance Test for Metropolis-Hastings

no code implementations19 Oct 2016 Daniel Seita, Xinlei Pan, Haoyu Chen, John Canny

We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data.

Fast Parallel SAME Gibbs Sampling on General Discrete Bayesian Networks

no code implementations19 Nov 2015 Daniel Seita, Haoyu Chen, John Canny

A fundamental task in machine learning and related fields is to perform inference on Bayesian networks.

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