3 citations · 6 across the 8 of their papers we have counts for
8 papers
OmniWheg: An Omnidirectional Wheel-Leg Transformable Robot
Ruixiang Cao, Jun Gu, Chen Yu +1
This paper presents the design, analysis, and performance evaluation of an omnidirectional transformable wheel-leg robot called OmniWheg. We design a novel mechanism consisting of…
Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning
Chen Yu, Andre Rosendo
While quadruped robots usually have good stability and load capacity, bipedal robots offer a higher level of flexibility / adaptability to different tasks and environments. A multi…
Rearranging the Environment to Maximize Energy with a Robotic Circuit Drawing
Xianglong Tan, Zhikang Liu, Chen Yu +1
Robots with the ability to actively acquire power from surroundings will be greatly beneficial for long-term autonomy and to survive in uncertain environments. In this work, we pre…
Scaffolded Learning of In-place Trotting Gait for a Quadruped Robot with Bayesian Optimization
Keyan Zhai, Chu'an Li, Andre Rosendo
During learning trials, systems are exposed to different failure conditions which may break robotic parts before a safe behavior is discovered. Humans contour this problem by groun…
Proximal Policy Optimization Smoothed Algorithm
Wangshu Zhu, Andre Rosendo
Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrog…
CircuitBot: Learning to Survive with Robotic Circuit Drawing
Xianglong Tan, Weijie Lyu, Andre Rosendo
Robots with the ability to actively acquire power from surroundings will be greatly beneficial for long-term autonomy, and to survive in dynamic, uncertain environments. In this wo…
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations
Jingyi Huang, Yizheng Zhang, Fabio Giardina +1
Deep Reinforcement Learning (DRL) experiments are commonly performed in simulated environments due to the tremendous training sample demands from deep neural networks. In contrast,…
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals
Yizheng Zhang, Andre Rosendo
Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error. However, learning can be hindered if the goal of the…