1 citations · 1 across the 3 of their papers we have counts for
17 papers
Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning
Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Humans can learn a new manipulation task from one or two demonstrations and then perform it in a new room, with new objects, under new constraints. Modern robot imitation learning,…
TiPToP: A Modular Open-Vocabulary Robot Manipulation System That Plans
William Shen, Nishanth Kumar, Sahit Chintalapudi +8
We present TiPToP, a modular manipulation system that integrates pretrained foundation models with a GPU-accelerated Task and Motion Planner to solve tasks directly from RGB images…
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…
SceneComplete: Open-World 3D Scene Completion in Cluttered Real World Environments for Robot Manipulation
Aditya Agarwal, Gaurav Singh, Bipasha Sen +2
Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to av…
Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories
Sunshine Jiang, Xiaolin Fang, Nicholas Roy +3
Recent advances in diffusionflow-matching policies have enabled imitation learning of complex, multi-modal action trajectories. However, they are computationally expensive becau…
LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
Aidan Curtis, Hao Tang, Thiago Veloso +4
Partially Observable Markov Decision Processes (POMDPs) model decision making under uncertainty. While there are many approaches to approximately solving POMDPs, we aim to address…