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Found 3 papers, 2 papers with code
Resampling-free Particle Filters in High-dimensions
State estimation is crucial for the performance and safety of numerous robotic applications.
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments
Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training.
Collision Avoidance Robotics Via Meta-Learning (CARML)
This paper presents an approach to exploring a multi-objective reinforcement learning problem with Model-Agnostic Meta-Learning.
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