Robot Learning

Know Thyself: Transferable Visuomotor Control Through Robot-Awareness
Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman
Conservative Offline Distributional Reinforcement Learning
Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani
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.
Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier
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.
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman
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.
Karl Pertsch, Oleg Rybkin*, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
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.
Mike Lambeta, Po-Wei Chou, Stephen Tian, Brian Yang, Benjamin Maloon, Victoria Rose Most, Dave Stroud, Raymond Santos, Ahmad Byagowi, Gregg Kammerer, Dinesh Jayaraman, Roberto Calandra
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
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.
Brian Yang, Dinesh Jayaraman, Glen Berseth, Alexei Efros, Sergey Levine
MAVRIC: Morphology-Agnostic Visual Robotic Control
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?
Pim de Haan, Dinesh Jayaraman, Sergey Levine
Causal Confusion in Imitation Learning
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.
Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform 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!
Brian Yang, Jesse Zhang, Vitchyr Pong, Sergey Levine, Dinesh Jayaraman
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning