activity
20182022
most citedEncoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

5 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2022

Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors

Xi Wang, Gen Li, Yen-Ling Kuo +3

We present a method for inferring diverse 3D models of human-object interactions from images. Reasoning about how humans interact with objects in complex scenes from a single 2D im…

cs.RO20205 cited

Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

Yen-Ling Kuo, Boris Katz, Andrei Barbu

We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LT…

cs.CL2020

Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach

Yu-Siang Wang, Yen-Ling Kuo, Boris Katz

We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to con…

cs.RO2020

Deep compositional robotic planners that follow natural language commands

Yen-Ling Kuo, Boris Katz, Andrei Barbu

We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and…

cs.RO2018

Deep sequential models for sampling-based planning

Yen-Ling Kuo, Andrei Barbu, Boris Katz

We demonstrate how a sequence model and a sampling-based planner can influence each other to produce efficient plans and how such a model can automatically learn to take advantage…