Autonomous fall recovery
Deep reinforcement learning to acquire whole-body motor skills: standing-up policies for legged robots. The learning algorithm can successfully discover various recovery behaviors by exploiting whole-body contacts with the environment to achieve this dynamic and contact-rich task.
Learning obstacle negotiation and fall recovery for mobile robot
Anthropomorphic robotic hand manipulation and grasping
Haptic-guided tele-operation using Sigma.7 and Franka arm: sense interaction forces and exploit affordance.
Autonomous robotic manipulation: reactive grasping
Deep reinforcement learning for autonomous grasping: learned re-grasping behavior to handle slippage and disturbances.
Fine control of grasping force
Compliant force control for strawberry-picking.
Immersive virtual reality for manipulation
Immersive multimodal interface for controlling Shadow hands to handle and manipulate various objects:
immersive VR headset, optical markerless hand-tracking and haptic gloves.
Object detection and semantic segmentation
Robot perception for robot manipulation tasks such as grasping.
Robust walking and push recovery
Optimisation and predictive control to counterbalance external pushes.
Stabilization and dynamic walking of humanoids
Unified formulation of Model Predictive Control of legged robots: for both offline gait planning and online feedback control.
The method is able to overcome unmodeled pushes and terrain irregularities.