Search Results for author: Ashwin Balakrishna

Found 27 papers, 6 papers with code

Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

2 code implementations12 Feb 2024 Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, Dorsa Sadigh

Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning; adoption that has fueled a wealth of new models such as LLaVa, InstructBLIP, and PaLI-3.

Hallucination Object Localization +3

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

Dynamics-Aware Comparison of Learned Reward Functions

no code implementations ICLR 2022 Blake Wulfe, Ashwin Balakrishna, Logan Ellis, Jean Mercat, Rowan Mcallister, Adrien Gaidon

The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world.

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

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

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.

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.

Object Thompson Sampling

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.

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

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 Model Predictive Control

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.

Robotics

Cannot find the paper you are looking for? You can Submit a new open access paper.