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09/18/2023
General In-Hand Object Rotation with Vision and Touch
We introduce RotateIt, a system that enables fingertip-based object rota...
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09/18/2023
Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice
Traffic congestion in dense urban centers presents an economical and env...
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09/26/2022
Learning Self-Supervised Representations from Vision and Touch for Active Sliding Perception of Deformable Surfaces
Humans make extensive use of vision and touch as complementary senses, w...
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03/17/2022
Investigating Compounding Prediction Errors in Learned Dynamics Models
Accurately predicting the consequences of agents' actions is a key prere...
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01/11/2022
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
The combination of Reinforcement Learning (RL) with deep learning has le...
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11/03/2021
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks
The co-adaptation of robot morphology and behaviour becomes increasingly...
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07/20/2021
Active 3D Shape Reconstruction from Vision and Touch
Humans build 3D understandings of the world through active object explor...
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06/03/2021
Towards Learning to Play Piano with Dexterous Hands and Touch
The virtuoso plays the piano with passion, poetry and extraordinary tech...
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05/26/2021
PyTouch: A Machine Learning Library for Touch Processing
With the increased availability of rich tactile sensors, there is an equ...
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04/20/2021
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
Model-based reinforcement learning is a compelling framework for data-ef...
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02/26/2021
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) is a promising framework for l...
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02/19/2021
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Accuracy and generalization of dynamics models is key to the success of ...
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12/16/2020
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Accurately predicting the dynamics of robotic systems is crucial for mod...
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12/15/2020
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
Simulators perform an important role in prototyping, debugging and bench...
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08/27/2020
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Hierarchical learning has been successful at learning generalizable loco...
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07/20/2020
Active MR k-space Sampling with Reinforcement Learning
Deep learning approaches have recently shown great promise in accelerati...
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07/07/2020
3D Shape Reconstruction from Vision and Touch
When a toddler is presented a new toy, their instinctual behaviour is to...
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06/18/2020
Learning Invariant Representations for Reinforcement Learning without Reconstruction
We study how representation learning can accelerate reinforcement learni...
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06/10/2020
Learning to Play Table Tennis From Scratch using Muscular Robots
Dynamic tasks like table tennis are relatively easy to learn for humans ...
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05/29/2020
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
Despite decades of research, general purpose in-hand manipulation remain...
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05/07/2020
Plan2Vec: Unsupervised Representation Learning by Latent Plans
In this paper we introduce plan2vec, an unsupervised representation lear...
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04/23/2020
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Transporting suspended payloads is challenging for autonomous aerial veh...
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03/21/2020
Adversarial Continual Learning
Continual learning aims to learn new tasks without forgetting previously...
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03/16/2020
OmniTact: A Multi-Directional High Resolution Touch Sensor
Incorporating touch as a sensing modality for robots can enable finer an...
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02/11/2020
Objective Mismatch in Model-based Reinforcement Learning
Model-based reinforcement learning (MBRL) has been shown to be a powerfu...
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01/31/2020
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is a popular approach to optimize expensive-t...
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11/15/2019
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
Humans and animals are capable of quickly learning new behaviours to sol...
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09/26/2019
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Learning to locomote to arbitrary goals on hardware remains a challengin...
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05/03/2019
Data-efficient Learning of Morphology and Controller for a Microrobot
Robot design is often a slow and difficult process requiring the iterati...
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04/07/2019
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
High-speed and high-acceleration movements are inherently hard to contro...
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03/11/2019
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Touch sensing is widely acknowledged to be important for dexterous robot...
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03/08/2019
Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching
Much of the literature on robotic perception focuses on the visual modal...
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02/19/2019
Fast Neural Network Verification via Shadow Prices
To use neural networks in safety-critical settings it is paramount to pr...
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01/11/2019
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement learning
Generating low-level robot controllers often requires manual parameters ...
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05/30/2018
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Model-based reinforcement learning (RL) algorithms can attain excellent ...
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05/28/2018
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
For humans, the process of grasping an object relies heavily on rich tac...
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03/01/2018
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
The design of gaits for robot locomotion can be a daunting process which...
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10/16/2017
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
A successful grasp requires careful balancing of the contact forces. Ded...
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09/10/2017
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Reinforcement Learning is divided in two main paradigms: model-free and ...
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02/24/2014
