Many experimental works have observed that generalization in deep RL appears to be difficult: although RL agents can learn to perform very complex tasks, they don’t seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. In this blog post, we will aim…
An example of our method deployed on a Clearpath Jackal ground robot (left) exploring a suburban environment to find a visual target (inset). (Right) Egocentric observations of the robot. Imagine you’re in an unfamiliar neighborhood with no house numbers and I give you a photo that I took a few days ago of my house,…
Many experimental works have observed that generalization in deep RL appears to be difficult: although RL agents can learn to perform very complex tasks, they don’t seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. In this blog post, we will aim…
Fig. 1: The BRIDGE dataset contains 7200 demonstrations of kitchen-themed manipulation tasks across 71 tasks in 10 domains. Note that any GIF compression artifacts in this animation are not present in the dataset itself. When we apply robot learning methods to real-world systems, we must usually collect new datasets for every task, every robot, and…
Processing raw sensory inputs is crucial for applying deep RL algorithms to real-world problems. For example, autonomous vehicles must make decisions about how to drive safely given information flowing from cameras, radar, and microphones about the conditions of the road, traffic signals, and other cars and pedestrians. However, direct “end-to-end” RL that maps sensor data…
Sequence Modeling Solutions for Reinforcement Learning Problems Long-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model. Modern machine learning success stories often have one thing in common: they use methods that scale gracefully with ever-increasing amounts of data. This is particularly clear from recent advances in sequence modeling,…
By Lance Eliot, the AI Trends Insider Are there things that we must not know? This is an age-old question. Some assert that there is the potential for knowledge that ought to not be known. In other words, there are ideas, concepts, or mental formulations that should we become aware of that knowledge it could be…
By AI Trends Staff Advances in the AI behind speech recognition are driving growth in the market, attracting venture capital and funding startups, posing challenges to established players. The growing acceptance and use of speech recognition devices are driving the market, which according to an estimate by Meticulous Research is expected to reach $26.8 billion…
By John P. Desmond, AI Trends Editor Two experiences of how AI developers within the federal government are pursuing AI accountability practices were outlined at the AI World Government event held virtually and in-person this week in Alexandria, Va. Taka Ariga, chief data scientist and director, US Government Accountability Office Taka Ariga, chief data scientist and director at the US Government Accountability…
By John P. Desmond, AI Trends Editor AI is more accessible to young people in the workforce who grew up as ‘digital natives’ with Alexa and self-driving cars as part of the landscape, giving them expectations grounded in their experience of what is possible. That idea set the foundation for a panel discussion at AI World…