Publications (64)
From Simple to Complex Skills: The Case of In-Hand Object Reorientation
Haozhi Qi, Brent Yi, Mike Lambeta +3
Learning policies in simulation and transferring them to the real world has become a promising approach in dexterous manipulation. However, bridging the sim-to-real gap for each ne…
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua, Roberto Calandra, Rowan McAllister +1
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. T…
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Sudharshan Suresh, Haozhi Qi, Tingfan Wu +9
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, suc…
Data-efficient Learning of Morphology and Controller for a Microrobot
Thomas Liao, Grant Wang, Brian Yang +4
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most rob…
Investigating Compounding Prediction Errors in Learned Dynamics Models
Nathan Lambert, Kristofer Pister, Roberto Calandra
Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which rel…
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
Roberto Calandra, Andrew Owens, Dinesh Jayaraman +5
For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus can…