2k citations · 3.7k across the 51 of their papers we have counts for
8 papers · 1 filter
Policies Modulating Trajectory Generators
Atil Iscen, Ken Caluwaerts, Jie Tan +4
We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both…
Autonomous Control of a Tendon-driven Robotic Limb with Elastic Elements Reveals that Added Elasticity can Enhance Learning
Ali Marjaninejad, Jie Tan, Francisco J. Valero-Cuevas
Passive elastic elements can contribute to stability, energetic efficiency, and impact absorption in both biological and robotic systems. They also add dynamical complexity which m…
Learning Fast Adaptation with Meta Strategy Optimization
Wenhao Yu, Jie Tan, Yunfei Bai +2
The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-l…
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Xinlei Pan, Tingnan Zhang, Brian Ichter +3
Imitation learning is a popular approach for training visual navigation policies. However, collecting expert demonstrations for legged robots is challenging as these robots can be…
Data Efficient Reinforcement Learning for Legged Robots
Yuxiang Yang, Ken Caluwaerts, Atil Iscen +3
We present a model-based framework for robot locomotion that achieves walking based on only 4.5 minutes (45,000 control steps) of data collected on a quadruped robot. To accurately…
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Qiuhua Huang, Renke Huang, Weituo Hao +3
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based…
NoRML: No-Reward Meta Learning
Yuxiang Yang, Ken Caluwaerts, Atil Iscen +2
Efficiently adapting to new environments and changes in dynamics is critical for agents to successfully operate in the real world. Reinforcement learning (RL) based approaches typi…
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods…