3 citations · 5 across the 4 of their papers we have counts for
8 papers
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression
Fernando Acero, Zhibin Li
Recent advancements in reinforcement learning (RL) have led to remarkable achievements in robot locomotion capabilities. However, the complexity and ``black-box'' nature of neural…
Hierarchical generative modelling for autonomous robots
Kai Yuan, Noor Sajid, Karl Friston +1
Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundame…
Learning Quadruped Locomotion using Bio-Inspired Neural Networks with Intrinsic Rhythmicity
Chuanyu Yang, Can Pu, Tianqi Wei +2
Biological studies reveal that neural circuits located at the spinal cord called central pattern generator (CPG) oscillates and generates rhythmic signals, which are the underlying…
Dexterous In-Hand Manipulation of Slender Cylindrical Objects through Deep Reinforcement Learning with Tactile Sensing
Wenbin Hu, Bidan Huang, Wang Wei Lee +3
Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of sma…
Instance-wise Grasp Synthesis for Robotic Grasping
Yucheng Xu, Mohammadreza Kasaei, Hamidreza Kasaei +1
Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance fo…
Agile and Versatile Robot Locomotion via Kernel-based Residual Learning
Milo Carroll, Zhaocheng Liu, Mohammadreza Kasaei +1
This work developed a kernel-based residual learning framework for quadrupedal robotic locomotion. Initially, a kernel neural network is trained with data collected from an MPC con…