Roberto Calandra

research

∙ 09/18/2023

General In-Hand Object Rotation with Vision and Touch

We introduce RotateIt, a system that enables fingertip-based object rota...

0 Haozhi Qi, et al. ∙

research

∙ 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...

0 Johannes V. S. Busch, et al. ∙

research

∙ 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...

0 Justin Kerr, et al. ∙

research

∙ 03/17/2022

Investigating Compounding Prediction Errors in Learned Dynamics Models

Accurately predicting the consequences of agents' actions is a key prere...

0 Nathan Lambert, et al. ∙

research

∙ 01/11/2022

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

The combination of Reinforcement Learning (RL) with deep learning has le...

38 Jack Parker-Holder, et al. ∙

research

∙ 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...

16 Kevin Sebastian Luck, et al. ∙

research

∙ 07/20/2021

Active 3D Shape Reconstruction from Vision and Touch

Humans build 3D understandings of the world through active object explor...

6 Edward J. Smith, et al. ∙

research

∙ 06/03/2021

Towards Learning to Play Piano with Dexterous Hands and Touch

The virtuoso plays the piano with passion, poetry and extraordinary tech...

0 Huazhe Xu, et al. ∙

research

∙ 05/26/2021

PyTouch: A Machine Learning Library for Touch Processing

With the increased availability of rich tactile sensors, there is an equ...

0 Mike Lambeta, et al. ∙

research

∙ 04/20/2021

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

Model-based reinforcement learning is a compelling framework for data-ef...

0 Luis Pineda, et al. ∙

research

∙ 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...

0 Baohe Zhang, et al. ∙

research

∙ 02/19/2021

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Accuracy and generalization of dynamics models is key to the success of ...

2 Manan Tomar, et al. ∙

research

∙ 12/16/2020

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Accurately predicting the dynamics of robotic systems is crucial for mod...

0 Nathan O. Lambert, et al. ∙

research

∙ 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...

16 Shaoxiong Wang, et al. ∙

research

∙ 08/27/2020

Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion

Hierarchical learning has been successful at learning generalizable loco...

0 Tianyu Li, et al. ∙

research

∙ 07/20/2020

Active MR k-space Sampling with Reinforcement Learning

Deep learning approaches have recently shown great promise in accelerati...

5 Luis Pineda, et al. ∙

research

∙ 07/07/2020

3D Shape Reconstruction from Vision and Touch

When a toddler is presented a new toy, their instinctual behaviour is to...

7 Edward J. Smith, et al. ∙

research

∙ 06/18/2020

Learning Invariant Representations for Reinforcement Learning without Reconstruction

We study how representation learning can accelerate reinforcement learni...

9 Amy Zhang, et al. ∙

research

∙ 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 ...

0 Dieter Büchler, et al. ∙

research

∙ 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...

11 Mike Lambeta, et al. ∙

research

∙ 05/07/2020

Plan2Vec: Unsupervised Representation Learning by Latent Plans

In this paper we introduce plan2vec, an unsupervised representation lear...

8 Ge Yang, et al. ∙

research

∙ 04/23/2020

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

Transporting suspended payloads is challenging for autonomous aerial veh...

1 Suneel Belkhale, et al. ∙

research

∙ 03/21/2020

Adversarial Continual Learning

Continual learning aims to learn new tasks without forgetting previously...

8 Sayna Ebrahimi, et al. ∙

research

∙ 03/16/2020

OmniTact: A Multi-Directional High Resolution Touch Sensor

Incorporating touch as a sensing modality for robots can enable finer an...

2 Akhil Padmanabha, et al. ∙

research

∙ 02/11/2020

Objective Mismatch in Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) has been shown to be a powerfu...

0 Nathan Lambert, et al. ∙

research

∙ 01/31/2020

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Bayesian optimization (BO) is a popular approach to optimize expensive-t...

16 Benjamin Letham, et al. ∙

research

∙ 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...

14 Kevin Sebastian Luck, et al. ∙

research

∙ 09/26/2019

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

Learning to locomote to arbitrary goals on hardware remains a challengin...

0 Tianyu Li, et al. ∙

research

∙ 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...

14 Thomas Liao, et al. ∙

research

∙ 04/07/2019

Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

High-speed and high-acceleration movements are inherently hard to contro...

0 Dieter Büchler, et al. ∙

research

∙ 03/11/2019

Manipulation by Feel: Touch-Based Control with Deep Predictive Models

Touch sensing is widely acknowledged to be important for dexterous robot...

2 Stephen Tian, et al. ∙

research

∙ 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...

0 Justin Lin, et al. ∙

research

∙ 02/19/2019

Fast Neural Network Verification via Shadow Prices

To use neural networks in safety-critical settings it is paramount to pr...

0 Vicenc Rubies Royo, et al. ∙

research

∙ 01/11/2019

Low Level Control of a Quadrotor with Deep Model-Based Reinforcement learning

Generating low-level robot controllers often requires manual parameters ...

0 Nathan O. Lambert, et al. ∙

research

∙ 05/30/2018

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Model-based reinforcement learning (RL) algorithms can attain excellent ...

0 Kurtland Chua, et al. ∙

research

∙ 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...

6 Roberto Calandra, et al. ∙

research

∙ 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...

0 Brian Yang, et al. ∙

research

∙ 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...

0 Roberto Calandra, et al. ∙

research

∙ 09/10/2017

MBMF: Model-Based Priors for Model-Free Reinforcement Learning

Reinforcement Learning is divided in two main paradigms: model-free and ...

0 Somil Bansal, et al. ∙

research

∙ 02/24/2014

Manifold Gaussian Processes for Regression

Off-the-shelf Gaussian Process (GP) covariance functions encode smoothne...

0 Roberto Calandra, et al. ∙

Roberto Calandra

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