news    views    podcast    learn    |    about    contribute     republish    

BAIR Blog


Homepage

The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, and robotics. BAIR includes over two dozen faculty and more than a hundred graduate students pursuing research on fundamental advances in the above areas as well as cross-cutting themes including multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities. The BAIR Blog provides an accessible, general-audience medium for BAIR researchers to communicate research findings, perspectives on the field, and various updates. Posts are written by students, post-docs, and faculty in BAIR, and are intended to provide relevant and timely discussion of research findings and results, both to experts and the general audience.



by   -   October 24, 2018

By Daniel Seita, Jeff Mahler, Mike Danielczuk, Matthew Matl, and Ken Goldberg

This post explores two independent innovations and the potential for combining them in robotics. Two years before the AlexNet results on ImageNet were released in 2012, Microsoft rolled out the Kinect for the X-Box. This class of low-cost depth sensors emerged just as Deep Learning boosted Artificial Intelligence by accelerating performance of hyper-parametric function approximators leading to surprising advances in image classification, speech recognition, and language translation.

by   -   October 18, 2018

By Xue Bin (Jason) Peng and Angjoo Kanazawa

Whether it’s everyday tasks like washing our hands or stunning feats of acrobatic prowess, humans are able to learn an incredible array of skills by watching other humans. With the proliferation of publicly available video data from sources like YouTube, it is now easier than ever to find video clips of whatever skills we are interested in.

by   -   September 10, 2018

...

In this post, we demonstrate how deep reinforcement learning (deep RL) can be used to learn how to control dexterous hands for a variety of manipulation tasks. We discuss how such methods can learn to make use of low-cost hardware, can be implemented efficiently, and how they can be complemented with techniques such as demonstrations and simulation to accelerate learning.

by   -   August 9, 2018

By John Miller

An earlier version of this post was published on Off the Convex Path. It is reposted here with the author’s permission.

In the last few years, deep learning practitioners have proposed a litany of different sequence models. Although recurrent neural networks were once the tool of choice, now models like the autoregressive Wavenet or the Transformer are replacing RNNs on a diverse set of tasks. In this post, we explore the trade-offs between recurrent and feed-forward models.

by   -   June 29, 2018

By Tianhe Yu and Chelsea Finn

Learning a new skill by observing another individual, the ability to imitate, is a key part of intelligence in human and animals. Can we enable a robot to do the same, learning to manipulate a new object by simply watching a human manipulating the object just as in the video below?

by   -   June 1, 2018

By Fisher Yu

TL;DR, we released the largest and most diverse driving video dataset with richannotations called BDD100K. You can access the data for research now at http://bdd-data.berkeley.edu. We haverecently released an arXivreport on it. And there is still time to participate in our CVPR 2018 challenges!

by   -   June 1, 2018

By Vitchyr Pong
You’ve decided that you want to bike from your house by UC Berkeley to the Golden Gate Bridge. It’s a nice 20 mile ride, but there’s a problem: you’ve never ridden a bike before!

by   -   April 24, 2018

By Siddharth Reddy

Imagine a drone pilot remotely flying a quadrotor, using an onboard camera to navigate and land. Unfamiliar flight dynamics, terrain, and network latency can make this system challenging for a human to control. One approach to this problem is to train an autonomous agent to perform tasks like patrolling and mapping without human intervention. This strategy works well when the task is clearly specified and the agent can observe all the information it needs to succeed. Unfortunately, many real-world applications that involve human users do not satisfy these conditions: the user’s intent is often private information that the agent cannot directly access, and the task may be too complicated for the user to precisely define. For example, the pilot may want to track a set of moving objects (e.g., a herd of animals) and change object priorities on the fly (e.g., focus on individuals who unexpectedly appear injured). Shared autonomy addresses this problem by combining user input with automated assistance; in other words, augmenting human control instead of replacing it.

by   -   April 11, 2018

Motion control problems have become standard benchmarks for reinforcement learning, and deep RL methods have been shown to be effective for a diverse suite of tasks ranging from manipulation to locomotion. However, characters trained with deep RL often exhibit unnatural behaviours, bearing artifacts such as jittering, asymmetric gaits, and excessive movement of limbs. Can we train our characters to produce more natural behaviours?

by   -   February 8, 2018

impedance_control

Humans physically interact with each other every day – from grabbing someone’s hand when they are about to spill their drink, to giving your friend a nudge to steer them in the right direction, physical interaction is an intuitive way to convey information about personal preferences and how to perform a task correctly.

by   -   December 31, 2017

By Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, and Bo Li based on recent research by Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, Dawn Song, and Florian Tramèr.

Deep neural networks (DNNs) have enabled great progress in a variety of application areas, including image processing, text analysis, and speech recognition. DNNs are also being incorporated as an important component in many cyber-physical systems. For instance, the vision system of a self-driving car can take advantage of DNNs to better recognize pedestrians, vehicles, and road signs. However, recent research has shown that DNNs are vulnerable to adversarial examples: Adding carefully crafted adversarial perturbations to the inputs can mislead the target DNN into mislabeling them during run time. Such adversarial examples raise security and safety concerns when applying DNNs in the real world. For example, adversarially perturbed inputs could mislead the perceptual systems of an autonomous vehicle into misclassifying road signs, with potentially catastrophic consequences.

by   -   December 24, 2017

By Carlos Florensa

Reinforcement Learning (RL) is a powerful technique capable of solving complex tasks such as locomotion, Atari games, racing games, and robotic manipulation tasks, all through training an agent to optimize behaviors over a reward function. There are many tasks, however, for which it is hard to design a reward function that is both easy to train and that yields the desired behavior once optimized.

by   -   December 18, 2017

By Changliu Liu, Masayoshi Tomizuka

Democratization of Robots in Factories

In modern factories, human workers and robots are two major workforces. For safety concerns, the two are normally separated with robots confined in metal cages, which limits the productivity as well as the flexibility of production lines. In recent years, attention has been directed to remove the cages so that human workers and robots may collaborate to create a human-robot co-existing factory.

by   -   December 7, 2017


By Sylvia Herbert, David Fridovich-Keil, and Claire Tomlin

The Problem: Fast and Safe Motion Planning

Real time autonomous motion planning and navigation is hard, especially when we care about safety. This becomes even more difficult when we have systems with complicated dynamics, external disturbances (like wind), and a priori unknown environments. Our goal in this work is to “robustify” existing real-time motion planners to guarantee safety during navigation of dynamic systems.

by   -   December 1, 2017

By Anusha Nagabandi and Gregory Kahn

Enabling robots to act autonomously in the real-world is difficult. Really, really difficult. Even with expensive robots and teams of world-class researchers, robots still have difficulty autonomously navigating and interacting in complex, unstructured environments.