198 citations · 459 across the 36 of their papers we have counts for
37 papers
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…
Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction
Quentin Rouxel, Ruoshi Wen, Zhibin Li +3
This short paper outlines two recent works on multi-contact teleoperation and the development of the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) framework. SEIKO…
Identifying Important Sensory Feedback for Learning Locomotion Skills
Wanming Yu, Chuanyu Yang, Christopher McGreavy +5
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings. While the selection of state observations is crucial, there…
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…
Perceptive Locomotion with Controllable Pace and Natural Gait Transitions Over Uneven Terrains
Daniel Chee Hian Tan, Jenny Zhang, Michael +2
This work developed a learning framework for perceptive legged locomotion that combines visual feedback, proprioceptive information, and active gait regulation of foot-ground conta…
Real-time Digital Double Framework to Predict Collapsible Terrains for Legged Robots
Garen Haddeler, Hari P. Palanivelu, Yung Chuen Ng +5
Inspired by the digital twinning systems, a novel real-time digital double framework is developed to enhance robot perception of the terrain conditions. Based on the very same phys…
Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics
Carlo Tiseo, Quentin Rouxel, Martin Asenov +4
Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the pa…
Multi-Contact Motion Retargeting using Whole-body Optimization of Full Kinematics and Sequential Force Equilibrium
Quentin Rouxel, Kai Yuan, Ruoshi Wen +1
This paper presents a multi-contact motion adaptation framework that enables teleoperation of high degree-of-freedom (DoF) robots, such as quadrupeds and humanoids, for loco-manipu…
Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control
Ruoshi Wen, Quentin Rouxel, Michael Mistry +2
This work developed collaborative bimanual manipulation for reliable and safe human-robot collaboration, which allows remote and local human operators to work interactively for bim…
Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains
Zhaocheng Liu, Fernando Acero, Zhibin Li
Legged robots are becoming increasingly powerful and popular in recent years for their potential to bring the mobility of autonomous agents to the next level. This work presents a…
Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations
Fernando Acero, Kai Yuan, Zhibin Li
To proactively navigate and traverse various terrains, active use of visual perception becomes indispensable. We aim to investigate the feasibility and performance of using sparse…
Accessibility-Based Clustering for Efficient Learning of Locomotion Skills
Chong Zhang, Wanming Yu, Zhibin Li
For model-free deep reinforcement learning of quadruped locomotion, the initialization of robot configurations is crucial for data efficiency and robustness. This work focuses on a…
Fine Manipulation and Dynamic Interaction in Haptic Teleoperation
Carlo Tiseo, Quentin Rouxel, Zhibin Li +1
The teleoperation of robots enables remote intervention in distant and dangerous tasks without putting the operator in harm's way. However, remote operation faces fundamental chall…
Robust Impedance Control for Dexterous Interaction Using Fractal Impedance Controller with IK-Optimisation
Carlo Tiseo, Quentin Rouxel, Zhibin Li +1
Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human moveme…
Learning Autonomous Mobility Using Real Demonstration Data
Jiacheng Gu, Zhibin Li
This work proposed an efficient learning-based framework to learn feedback control policies from human teleoperated demonstrations, which achieved obstacle negotiation, staircase t…
Metrics for 3D Object Pointing and Manipulation in Virtual Reality
Eleftherios Triantafyllidis, Wenbin Hu, Christopher McGreavy +1
Assessing the performance of human movements during teleoperation and virtual reality is a challenging problem, particularly in 3D space due to complex spatial settings. Despite th…
Reachability Map for Diverse Balancing Strategies and Energy Efficient Stepping of Humanoids
Christopher McGreavy, Zhibin Li
In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot b…
Trajectory optimization for contact-rich motions using implicit differential dynamic programming
Iordanis Chatzinikolaidis, Zhibin Li
This paper presents a novel approach using sensitivity analysis for generalizing Differential Dynamic Programming (DDP) to systems characterized by implicit dynamics, such as those…
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments
Timothée Anne, Jack Wilkinson, Zhibin Li
This work developed a meta-learning approach that adapts the control policy on the fly to different changing conditions for robust locomotion. The proposed method constantly update…
Multi-expert learning of adaptive legged locomotion
Chuanyu Yang, Kai Yuan, Qiuguo Zhu +2
Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations. We propose a Multi-Expert Learning Architecture (MELA) that learns to ge…
Learning Hybrid Locomotion Skills -- Learn to Exploit Residual Dynamics and Modulate Model-based Gait Control
Mohammadreza Kasaei, Miguel Abreu, Nuno Lau +3
This work aims to combine machine learning and control approaches for legged robots, and developed a hybrid framework to achieve new capabilities of balancing against external pert…