3 citations · 4 across the 6 of their papers we have counts for
6 papers
Guided Exploration for Efficient Relational Model Learning
Annie Feng, Nishanth Kumar, Tomas Lozano-Perez +1
Efficient exploration is critical for learning relational models in large-scale environments with complex, long-horizon tasks. Random exploration methods often collect redundant or…
From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Ashay Athalye, Nishanth Kumar, Tom Silver +4
Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing seque…
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
Yichao Liang, Nishanth Kumar, Hao Tang +5
Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensori…
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning
Alicia Li, Nishanth Kumar, Tomás Lozano-Pérez +1
The real world is unpredictable. Therefore, to solve long-horizon decision-making problems with autonomous robots, we must construct agents that are capable of adapting to changes…
Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction
Aidan Curtis, Nishanth Kumar, Jing Cao +2
Recent developments in pretrained large language models (LLMs) applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended go…
Practice Makes Perfect: Planning to Learn Skill Parameter Policies
Nishanth Kumar, Tom Silver, Willie McClinton +5
One promising approach towards effective robot decision making in complex, long-horizon tasks is to sequence together parameterized skills. We consider a setting where a robot is i…