Uploaded August 2021 | Updated September 2026, 1 hour ago
Embodied AI Lecture Series @ PRIOR
For previous recordings and upcoming talks visit prior.allenai.org/lectures
Task Planning and Reinforcement Learning for General-Purpose Service Robots
Yuqian Jiang • UT Austin • (8/6/21)
Despite recent progress in the capabilities of autonomous robots, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This talk will present our recent work to answer the following question: how can symbolic planning and reinforcement learning be combined to create general-purpose service robots that reason about high-level actions and adapt to the real world? The problem will be approached from two directions. First, I will introduce planning algorithms that adapt to the environment by learning and exchanging knowledge with other agents. These methods allow robots to plan in open-world scenarios, to plan around other robots while avoiding conflicts and realizing synergies, and to learn action costs throughout executions in the real world. Second, I will present reinforcement learning (RL) methods that leverage reasoning and planning, in order to address the challenges of maximizing the long-term average reward in continuing service robot tasks.
Embodied AI Lecture Series @ PRIOR
For previous recordings and upcoming talks visit prior.allenai.org/lectures
Task Planning and Reinforcement Learning for General-Purpose Service Robots
Yuqian Jiang • UT Austin • (8/6/21)
Despite recent progress in the capabilities of autonomous robots, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This talk will present our recent work to answer the following question: how can symbolic planning and reinforcement learning be combined to create general-purpose service robots that reason about high-level actions and adapt to the real world? The problem will be approached from two directions. First, I will introduce planning algorithms that adapt to the environment by learning and exchanging knowledge with other agents. These methods allow robots to plan in open-world scenarios, to plan around other robots while avoiding conflicts and realizing synergies, and to learn action costs throughout executions in the real world. Second, I will present reinforcement learning (RL) methods that leverage reasoning and planning, in order to address the challenges of maximizing the long-term average reward in continuing service robot tasks.
