328 citations · 1.9k across the 108 of their papers we have counts for
12 papers · 1 filter
A Berkeley View of Systems Challenges for AI
Ion Stoica, Dawn Song, Raluca Ada Popa +11
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies suc…
SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics
Vatsal Patel, Sanjay Krishnan, Aimee Goncalves +1
To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research Kit) to translate and ro…
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz +6
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation…
Learning Robust Bed Making using Deep Imitation Learning with DART
Michael Laskey, Chris Powers, Ruta Joshi +2
Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions. Automating bed-making has multiple technical challenges such as perception…
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Richard Liaw, Sanjay Krishnan, Animesh Garg +3
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This pape…
BoostClean: Automated Error Detection and Repair for Machine Learning
Sanjay Krishnan, Michael J. Franklin, Ken Goldberg +1
Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to differe…
DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations
Sanjay Krishnan, Roy Fox, Ion Stoica +1
An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work…
Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
Jeffrey Mahler, Matthew Matl, Xinyu Liu +3
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 o…
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
Daniel Seita, Sanjay Krishnan, Roy Fox +3
Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is c…
DART: Noise Injection for Robust Imitation Learning
Michael Laskey, Jonathan Lee, Roy Fox +2
One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy. A known problem with this "off-policy" approach is that…
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz +5
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…
Multi-Level Discovery of Deep Options
Roy Fox, Sanjay Krishnan, Ion Stoica +1
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 i…