Search Results for author: Brijen Thananjeyan

Found 23 papers, 5 papers with code

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

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo

1 code implementation30 Jun 2021 Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan, Mark Tjersland

However, the RGB-D baseline only grasps 35% of the hard (e. g., transparent) objects, while SimNet grasps 95%, suggesting that SimNet can enable robust manipulation of unknown objects, including transparent objects, in unknown environments.

Keypoint Detection object-detection +4

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).

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

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

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.

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.


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

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