Foundation reinforcement learning: towards embodied generalist agents with foundation prior assistance
Recently, people have shown that large-scale pre-training from diverse internet-scale data is
the key to building a generalist model, as witnessed in the natural language processing …
the key to building a generalist model, as witnessed in the natural language processing …
Reinforcement Learning with Foundation Priors: Let Embodied Agent Efficiently Learn on Its Own
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks.
However, it is challenging to apply the RL algorithms directly in the real world. For one thing …
However, it is challenging to apply the RL algorithms directly in the real world. For one thing …
Exploring Under Constraints with Model-Based Actor-Critic and Safety Filters
Applying reinforcement learning (RL) to learn effective policies on physical robots without
supervision remains challenging when it comes to tasks where safe exploration is critical …
supervision remains challenging when it comes to tasks where safe exploration is critical …
Sample-efficient and occlusion-robust reinforcement learning for robotic manipulation via multimodal fusion dualization and representation normalization
Recent advances in visual reinforcement learning (visual RL), which learns from high-
dimensional image observations, have narrowed the gap between state-based and image …
dimensional image observations, have narrowed the gap between state-based and image …
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
M Schneider, R Krug, N Vaskevicius, L Palmieri… - ar**
Intelligent vision control systems for surgical robots should adapt to unknown and diverse
objects while being robust to system disturbances. Previous methods did not meet these …
objects while being robust to system disturbances. Previous methods did not meet these …
Model-Free versus Model-Based Reinforcement Learning for Fixed-Wing UAV Attitude Control Under Varying Wind Conditions
This paper evaluates and compares the performance of model-free and model-based
reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using …
reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using …
Bisimulation metric for Model Predictive Control
Model-based reinforcement learning has shown promise for improving sample efficiency
and decision-making in complex environments. However, existing methods face challenges …
and decision-making in complex environments. However, existing methods face challenges …