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Found 14 papers, 7 papers with code
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
We provide theoretical analysis of our selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet.
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion.
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.
Efficient Online Reinforcement Learning with Offline Data
Sample efficiency and exploration remain major challenges in online reinforcement learning (RL).
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge.
B-Pref: Benchmarking Preference-Based Reinforcement Learning
However, it is difficult to quantify the progress in preference-based RL due to the lack of a commonly adopted benchmark.
Offline Meta-Reinforcement Learning with Online Self-Supervision
If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewards for different tasks, to meta-train policies that adapt to a variety of new tasks at meta-test time.
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
We also show that our method is able to utilize real-time human feedback to effectively prevent reward exploitation and learn new behaviors that are difficult to specify with standard reward functions.
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.
Identifying Locus of Control in Social Media Language
Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances.
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images.
Model-based Reinforcement Learning
reinforcement-learning
+1
