5 papers
Learning a Control Policy for Fall Prevention on an Assistive Walking Device
Visak C V Kumar, Sehoon Ha, Gergory Sawicki +1
Fall prevention is one of the most important components in senior care. We present a technique to augment an assistive walking device with the ability to prevent falls. Given an ex…
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…
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…
Learning to Walk via Deep Reinforcement Learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou +3
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domai…