AI Technology & Industry Review
In the new paper MO2: Model-Based Offline Options, a DeepMind research team introduces Model-Based Offline Options (MO2), an offline hindsight bottleneck options framework that supports sample-efficient option discovery over continuous state-action spaces for efficient skill transfer to new tasks.
A research team from Stanford University introduces BEHAVIOR, a benchmark for embodied AI with 100 realistic, diverse and complex everyday household activities in simulation. BEHAVIOR addresses challenges such as definition, instantiation in a simulator, and evaluation; and pushes the state-of-the-art by adding new types of state changes.
A research team from MIT and MIT-IBM Watson AI Lab proposes Curious Representation Learning (CRL), a framework that learns to understand the surrounding environment by training a reinforcement learning (RL) agent to maximize the error of a representation learner to gain an incentive to explore the environment.
Engineers have long struggled to teach robots how to perform actions that appear simple by human standards. Former OpenAI research scientist Peter Chen believes the solution is transforming such robots into teachable apprentices.
At last week’s Collaborative Robotics and Advanced Vision Conference in San Jose, John Petry from Cognex stressed the need for robotic technology companies to provide maintainable deep learning-based solutions for their manufacturing customers.