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Found 9 papers, 3 papers with code
The Essentials of AI for Life and Society: An AI Literacy Course for the University Community
We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin.
Grounded Curriculum Learning
The high cost of real-world data for robotics Reinforcement Learning (RL) leads to the wide usage of simulators.
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
To address the inefficiency of tracking distant navigation goals, we introduce a hierarchical locomotion controller that combines a classical planner tasked with planning waypoints to reach a faraway global goal location, and an RL-based policy trained to follow these waypoints by generating low-level motion commands.
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks
Multitask Reinforcement Learning (MTRL) approaches have gained increasing attention for its wide applications in many important Reinforcement Learning (RL) tasks.
LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning
Instead, this paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales, with a latent variable called a reasoning skill.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation.
Causal Dynamics Learning for Task-Independent State Abstraction
Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states.
A Scavenger Hunt for Service Robots
In this problem, the goal is to find a set of objects as quickly as possible, given probability distributions of where they may be found.
