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Found 13 papers, 6 papers with code
Interactive Visual Task Learning for Robots
With both of these results we demonstrate the ability of our model to learn tasks and concepts in a continual learning setting on the robot.
Improved Inference of Human Intent by Combining Plan Recognition and Language Feedback
We propose a novel framework for plan and goal recognition in partially observable domains -- Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its belief in human progress by asking clarification questions about noisy sensor data and sub-optimal human actions.
Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains
In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory using language instructions.
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees
In each test, CA-RRT reached more states on average in the same number of iterations as weighted-RRT.
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer
Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training.
Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions
Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.
Learning to Follow Language Instructions with Compositional Policies
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions.
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy.
Robot Object Retrieval with Contextual Natural Language Queries
The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects and selects the object that best satisfies the task specified by the verb.
Grounding Language Attributes to Objects using Bayesian Eigenobjects
Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions.
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model.
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands.
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular.
