Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Text2motion: From natural language instructions to feasible plans
Abstract We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …
Learning neuro-symbolic skills for bilevel planning
Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …
Embodied lifelong learning for task and motion planning
A robot deployed in a home over long stretches of time faces a true lifelong learning
problem. As it seeks to provide assistance to its users, the robot should leverage any …
problem. As it seeks to provide assistance to its users, the robot should leverage any …
Predicate invention for bilevel planning
Efficient planning in continuous state and action spaces is fundamentally hard, even when
the transition model is deterministic and known. One way to alleviate this challenge is to …
the transition model is deterministic and known. One way to alleviate this challenge is to …
Learning efficient abstract planning models that choose what to predict
An effective approach to solving long-horizon tasks in robotics domains with continuous
state and action spaces is bilevel planning, wherein a high-level search over an abstraction …
state and action spaces is bilevel planning, wherein a high-level search over an abstraction …
[PDF][PDF] Inventing relational state and action abstractions for effective and efficient bilevel planning
Effective and efficient planning in continuous state and action spaces is fundamentally hard,
even when the transition model is deterministic and known. One way to alleviate this …
even when the transition model is deterministic and known. One way to alleviate this …
Emerging Trends in Machine Learning assisted Optimization Techniques Across Intelligent Transportation Systems
Artificial intelligence (AI) plays a critical role in Intelligent Transport Systems (ITS) as urban
areas grow by processing data for safety enhancements, predictive analysis, and traffic …
areas grow by processing data for safety enhancements, predictive analysis, and traffic …
Relational decomposition for program synthesis
We introduce a novel approach to program synthesis that decomposes complex functional
tasks into simpler relational synthesis sub-tasks. We demonstrate the effectiveness of our …
tasks into simpler relational synthesis sub-tasks. We demonstrate the effectiveness of our …
Predicate Invention from Pixels via Pretrained Vision-Language Models
Our aim is to learn to solve long-horizon decision-making problems in highly-variable,
combinatorially-complex robotics domains given raw sensor input in the form of images …
combinatorially-complex robotics domains given raw sensor input in the form of images …