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Found 9 papers, 2 papers with code
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces.
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language.
Language-Informed Transfer Learning for Embodied Household Activities
For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users.
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks
Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy.
Deep R-Learning for Continual Area Sweeping
Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency.
Solving Service Robot Tasks: UT Austin Villa@Home 2019 Team Report
RoboCup@Home is an international robotics competition based on domestic tasks requiring autonomous capabilities pertaining to a large variety of AI technologies.
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Natural language understanding for robotics can require substantial domain- and platform-specific engineering.
Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots
In the outer loop, the plan is executed, and the robot learns from the execution experience via model-free RL, to further improve its task-motion plans.
Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems
PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems.
