no code implementations • 23 May 2023 • Alejandro Escontrela, Ademi Adeniji, Wilson Yan, Ajay Jain, Xue Bin Peng, Ken Goldberg, Youngwoon Lee, Danijar Hafner, Pieter Abbeel
A promising approach is to extract preferences for behaviors from unlabeled videos, which are widely available on the internet.
no code implementations • 16 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.
no code implementations • 14 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.
no code implementations • 13 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.
no code implementations • 27 Sep 2022 • Kaushik Shivakumar, Vainavi Viswanath, Anrui Gu, Yahav Avigal, Justin Kerr, Jeffrey Ichnowski, Richard Cheng, Thomas Kollar, Ken Goldberg
Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling.
1 code implementation • 22 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.
no code implementations • 22 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.
no code implementations • 16 Jul 2022 • Vainavi Viswanath, Kaushik Shivakumar, Justin Kerr, Brijen Thananjeyan, Ellen Novoseller, Jeffrey Ichnowski, Alejandro Escontrela, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg
Cables are ubiquitous in many settings and it is often useful to untangle them.
1 code implementation • 29 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.
1 code implementation • 28 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.
no code implementations • 1 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.
no code implementations • 21 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.
no code implementations • 28 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.
no code implementations • 9 Mar 2022 • Brijen Thananjeyan, Justin Kerr, Huang Huang, Joseph E. Gonzalez, Ken Goldberg
Large-scale semantic image annotation is a significant challenge for learning-based perception systems in robotics.
no code implementations • 8 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.
no code implementations • 22 Jan 2022 • Huang Huang, Michael Danielczuk, Chung Min Kim, Letian Fu, Zachary Tam, Jeffrey Ichnowski, Anelia Angelova, Brian Ichter, Ken Goldberg
Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency.
no code implementations • 7 Dec 2021 • Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan, Suraj Nair, Julian Ibarz, Jie Tan, Chelsea Finn, Ion Stoica, Ken Goldberg
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
1 code implementation • 11 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.
1 code implementation • 27 Oct 2021 • Jeffrey Ichnowski, Yahav Avigal, Justin Kerr, Ken Goldberg
The ability to grasp and manipulate transparent objects is a major challenge for robots.
no code implementations • 17 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?
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.
1 code implementation • 13 Jul 2021 • Shivin Devgon, Jeffrey Ichnowski, Michael Danielczuk, Daniel S. Brown, Ashwin Balakrishna, Shirin Joshi, Eduardo M. C. Rocha, Eugen Solowjow, Ken Goldberg
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly.
1 code implementation • 10 Jul 2021 • Albert Wilcox, Ashwin Balakrishna, Brijen Thananjeyan, Joseph E. Gonzalez, Ken Goldberg
We then present a new algorithm, Latent Space Safe Sets (LS3), which uses this representation for long-horizon tasks with sparse rewards.
no code implementations • 29 Jun 2021 • Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Jeffrey Ichnowski, Ellen Novoseller, Minho Hwang, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg
We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which operate alongside HULK, a high-level planner from prior work.
no code implementations • 11 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.
no code implementations • 6 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).
no code implementations • 4 Jun 2021 • Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Ellen Novoseller, Jeffrey Ichnowski, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg
Disentangling two or more cables requires many steps to remove crossings between and within cables.
no code implementations • 29 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.
no code implementations • 31 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.
no code implementations • 19 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.
no code implementations • 23 Dec 2020 • Minho Hwang, Brijen Thananjeyan, Daniel Seita, Jeffrey Ichnowski, Samuel Paradis, Danyal Fer, Thomas Low, Ken Goldberg
Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS).
no code implementations • 7 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.
no code implementations • 7 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.
no code implementations • 6 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".
no code implementations • 11 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.
no code implementations • 11 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.
no code implementations • 10 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.
no code implementations • 10 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.
no code implementations • 31 Oct 2020 • Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez
Second, we present an algorithm for a fixed deadline setting, where we are given a time deadline and need to maximize the probability of finding the best arm.
2 code implementations • 29 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.
no code implementations • 9 Oct 2020 • Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Daniel Seita, Ryan Hoque, Joseph E. Gonzalez, Ken Goldberg
We explore learning pixelwise correspondences between images of deformable objects in different configurations.
no code implementations • 20 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.
no code implementations • 31 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.
no code implementations • 20 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.
no code implementations • 28 Mar 2020 • Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Daniel Seita, Jennifer Grannen, Minho Hwang, Ryan Hoque, Joseph E. Gonzalez, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics.
2 code implementations • 19 Mar 2020 • Ryan Hoque, Daniel Seita, Ashwin Balakrishna, Aditya Ganapathi, Ajay Kumar Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery.
no code implementations • 19 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.
no code implementations • 5 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).
no code implementations • 3 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.
no code implementations • 3 Mar 2020 • Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Michael Laskey, Kevin Stone, Joseph E. Gonzalez, Ken Goldberg
We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation.
no code implementations • 15 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.
1 code implementation • 29 Dec 2019 • Roy Fox, Richard Shin, William Paul, Yitian Zou, Dawn Song, Ken Goldberg, Pieter Abbeel, Ion Stoica
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
no code implementations • 3 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.
1 code implementation • 23 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.
no code implementations • 8 Jul 2019 • Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Felix Li, Arsh Zahed, Joseph E. Gonzalez, Ken Goldberg
Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor.
no code implementations • 31 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
no code implementations • 4 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.
no code implementations • 19 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.
1 code implementation • 6 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.
2 code implementations • 26 Sep 2018 • Daniel Seita, Nawid Jamali, Michael Laskey, Ajay Kumar Tanwani, Ron Berenstein, Prakash Baskaran, Soshi Iba, John Canny, Ken Goldberg
We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points.
2 code implementations • 16 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.
Ranked #1 on Unseen Object Instance Segmentation on WISDOM
no code implementations • 16 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.
no code implementations • 9 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.
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.
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.
no code implementations • 15 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.
no code implementations • 7 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.
no code implementations • 4 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.
3 code implementations • 12 Oct 2017 • Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, Pieter Abbeel
Imitation learning is a powerful paradigm for robot skill acquisition.
1 code implementation • 19 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.
no code implementations • 19 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.
2 code implementations • 27 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.
no code implementations • 27 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.
no code implementations • 24 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.
no code implementations • 4 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.
no code implementations • 15 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.