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Found 7 papers, 4 papers with code
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
DROC is able to respond to a sequence of online language corrections that address failures in both high-level task plans and low-level skill primitives.
"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy
Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot: language is an input to a learned model that produces a meaningful, low-dimensional control space that the human can use to guide the robot.
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations
Specifically, we design a masked policy network with a binary mask to block certain modalities.
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Task specification is at the core of programming autonomous robots.
The EMPATHIC Framework for Task Learning from Implicit Human Feedback
We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories.
Human-Computer Interaction Robotics
Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Our analysis shows that UAIL outperforms existing data aggregation algorithms on a series of benchmark tasks.
Risk-Aware Active Inverse Reinforcement Learning
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning.
