539 citations · 2k across the 41 of their papers we have counts for
14 papers · 1 filter
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Nathan O. Lambert, Albert Wilcox, Howard Zhang +2
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-st…
TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
Shaoxiong Wang, Mike Lambeta, Po-Wei Chou +1
Simulators perform an important role in prototyping, debugging, and benchmarking new advances in robotics and learning for control. Although many physics engines exist, some aspect…
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Tianyu Li, Roberto Calandra, Deepak Pathak +3
Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional "latent" action u…
Active MR k-space Sampling with Reinforcement Learning
Luis Pineda, Sumana Basu, Adriana Romero +2
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing bette…
3D Shape Reconstruction from Vision and Touch
Edward J. Smith, Roberto Calandra, Adriana Romero +4
When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly…
Learning to Play Table Tennis From Scratch using Muscular Robots
Dieter Büchler, Simon Guist, Roberto Calandra +3
Dynamic tasks like table tennis are relatively easy to learn for humans but pose significant challenges to robots. Such tasks require accurate control of fast movements and precise…
Learning Invariant Representations for Reinforcement Learning without Reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra +2
We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstructio…
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
Mike Lambeta, Po-Wei Chou, Stephen Tian +9
Despite decades of research, general purpose in-hand manipulation remains one of the unsolved challenges of robotics. One of the contributing factors that limit current robotic man…
Plan2Vec: Unsupervised Representation Learning by Latent Plans
Ge Yang, Amy Zhang, Ari S. Morcos +3
In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image d…
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Suneel Belkhale, Rachel Li, Gregory Kahn +3
Transporting suspended payloads is challenging for autonomous aerial vehicles because the payload can cause significant and unpredictable changes to the robot's dynamics. These cha…
OmniTact: A Multi-Directional High Resolution Touch Sensor
Akhil Padmanabha, Frederik Ebert, Stephen Tian +3
Incorporating touch as a sensing modality for robots can enable finer and more robust manipulation skills. Existing tactile sensors are either flat, have small sensitive fields or…
Adversarial Continual Learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra +2
Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared st…
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan +1
Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focuse…
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Benjamin Letham, Roberto Calandra, Akshara Rai +1
Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spac…