Home assistants and smart displays are being sold in record numbers, but they are built wrong. They are designed with one person in mind: the owner. These technologies need to fit into the communal spaces where they are placed, like homes and offices. If they don’t fit, they will be unplugged and put away due…
OpenAI’s text generating system GPT-3 has captured mainstream attention. GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet. The output produced from this autocomplete bot can be used to manipulate people on social media and spew political propaganda, argue about…
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,…
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…
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…
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. Measures of generalization performance for neural networks trained on four different boolean functions (colors) with varying training set size. For both MSE (left) and learnability (right), theoretical predictions (curves) closely match true performance (dots). Deep learning has proven a stunning success for countless problems of interest, but this success belies the fact that,…
Diagram of MURAL, our method for learning uncertainty-aware rewards for RL. After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent’s uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip placement and playing…