Know Thyself: Transferable Visuomotor Control Through Robot-Awareness
April 2022
Conservative Offline Distributional Reinforcement Learning
December 2021
Long-horizon visual planning with goal-conditioned hierarchical predictors
To plan towards long-term goals through visual prediction, we propose a model based on two key ideas: (i) predict in a goal-conditioned way to restrict planning only to useful sequences, and (ii) recursively decompose the goal-conditioned prediction task into an increasingly fine series of subgoals.
December 2020
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
We learn reward functions in unsupervised object keypoint space, to allow us to follow third-person demonstrations with model-based RL.
November 2020
Cautious adaptation for reinforcement learning in safety-critical settings
How to train RL agents safely? We propose to pretrain a model-based agent in a mix of sandbox environments, then plan pessimistically when finetuning in the target environment.
July 2020
MAVRIC: Morphology-Agnostic Visual Robotic Control
We demonstrate visual control within 20 seconds on a robot with unknown morphology, from a single uncalibrated RGBD camera.
May 2020
Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation
We design and demonstrate a new tactile sensor for in-hand tactile manipulation in a robotic hand.
May 2020
Causal Confusion in Imitation Learning
"Causal confusion", where spurious correlates are mistaken to be causes of expert actions, is commonly prevalent in imitation learning, leading to counterintuitive results where additional information can lead to worse task performance. How might one address this?
December 2019
REPLAB: A reproducible low-cost arm benchmark for robotic learning
We propose a low-cost compact easily replicable hardware stack for manipulation tasks, that can be assembled within a few hours. We also provide implementations of robot learning algorithms for grasping (supervised learning) and reaching (reinforcement learning). Contributions invited!
May 2019
Manipulation by feel: Touch-based control with deep predictive models
High-resolution tactile sensing together with visual approaches to prediction and planning with deep neural networks enables high-precision tactile servoing tasks.
May 2019
Time-agnostic prediction: Predicting predictable video frames
In visual prediction tasks, letting your predictive model choose which times to predict does two things: (i) improves prediction quality, and (ii) leads to semantically coherent "bottleneck state" predictions, which are useful for planning.
April 2019
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
By exploiting high precision tactile sensing with deep learning, robots can effectively iteratively adjust their grasp configurations to boost grasping performance from 65% to 94%.
October 2018