2 citations · 3 across the 4 of their papers we have counts for
9 papers
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…
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…
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…
One-Shot Manipulation Strategy Learning by Making Contact Analogies
Yuyao Liu, Jiayuan Mao, Joshua Tenenbaum +2
We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive general…
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning
Xiaolin Fang, Bo-Ruei Huang, Jiayuan Mao +4
Generalization to novel object configurations and instances across diverse tasks and environments is a critical challenge in robotics. Keypoint-based representations have been prov…
Combining Planning and Diffusion for Mobility with Unknown Dynamics
Yajvan Ravan, Zhutian Yang, Tao Chen +2
Manipulation of large objects over long horizons (such as carts in a warehouse) is an essential skill for deployable robotic systems. Large objects require mobile manipulation whic…