Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces

no code implementations • 5 Jun 2022 • Dawei Chen, Samuel Yang-Zhao, John Lloyd, Kee Siong Ng

This paper introduces the factored conditional filter, a new filtering algorithm for simultaneously tracking states and estimating parameters in high-dimensional state spaces.

Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation

2 code implementations • 16 Jun 2021 • Alex Church, John Lloyd, Raia Hadsell, Nathan F. Lepora

Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs.

reinforcement-learning Reinforcement Learning (RL) +1

Towards integrated tactile sensorimotor control in anthropomorphic soft robotic hands

no code implementations • 5 Feb 2021 • Nathan F. Lepora, Andrew Stinchcombe, Chris Ford, Alfred Brown, John Lloyd, Manuel G. Catalano, Matteo Bianchi, Benjamin Ward-Cherrier

In this work, we report on the integrated sensorimotor control of the Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a soft biomimetic optical tactile sensor based on the human sense of touch.

Robotics

Goal-Driven Robotic Pushing Using Tactile and Proprioceptive Feedback

no code implementations • 3 Dec 2020 • John Lloyd, Nathan F. Lepora

We evaluate our method by pushing objects across planar and curved surfaces.

Robotics

Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard

1 code implementation • 6 Aug 2020 • Alex Church, John Lloyd, Raia Hadsell, Nathan F. Lepora

Artificial touch would seem well-suited for Reinforcement Learning (RL), since both paradigms rely on interaction with an environment.

reinforcement-learning Reinforcement Learning (RL)