activity
20152022
most citedSocial-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

113 citations · 909 across the 41 of their papers we have counts for

collaborators
Showing cs.AIShow all

6 papers · 1 filter

cs.AI2020

iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes

Bokui Shen, Fei Xia, Chengshu Li +12

We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interac…

cs.AI2020

Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter

Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni +4

When searching for objects in cluttered environments, it is often necessary to perform complex interactions in order to move occluding objects out of the way and fully reveal the o…

cs.AI2020

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Fei Xia, Chengshu Li, Roberto Martín-Martín +3

Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action s…

cs.AI20199 cited

Regression Planning Networks

Danfei Xu, Roberto Martín-Martín, De-An Huang +3

Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accura…

cs.AI2019

Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning

De-An Huang, Danfei Xu, Yuke Zhu +4

We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direct…

cs.AI2018

Gibson Env: Real-World Perception for Embodied Agents

Fei Xia, Amir Zamir, Zhi-Yang He +3

Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently l…