no code implementations • 21 Apr 2024 • Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila Fiete
State estimation is crucial for the performance and safety of numerous robotic applications.
1 code implementation • 31 Dec 2021 • Abhiram Iyer, Karan Grewal, Akash Velu, Lucas Oliveira Souza, Jeremy Forest, Subutai Ahmad
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.
1 code implementation • 16 Jul 2020 • Abhiram Iyer, Aravind Mahadevan
This paper presents an approach to exploring a multi-objective reinforcement learning problem with Model-Agnostic Meta-Learning.