198 citations · 316 across the 11 of their papers we have counts for
11 papers
ADAPT: Actively Discovering and Adapting to Preferences for any Task
Maithili Patel, Xavier Puig, Ruta Desai +4
Assistive agents should be able to perform under-specified long-horizon tasks while respecting user preferences. We introduce Actively Discovering and Adapting to Preferences for a…
RobotMover: Learning to Move Large Objects From Human Demonstrations
Tianyu Li, Joanne Truong, Jimmy Yang +4
Moving large objects, such as furniture or appliances, is a critical capability for robots operating in human environments. This task presents unique challenges, including whole-bo…
PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks
Matthew Chang, Gunjan Chhablani, Alexander Clegg +17
We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhib…
IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience
Joanne Truong, April Zitkovich, Sonia Chernova +4
We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual navigation approach, trained solely in simulated short-range indoor environments, and demonstrates zero-shot…
ViNL: Visual Navigation and Locomotion Over Obstacles
Simar Kareer, Naoki Yokoyama, Dhruv Batra +2
We present Visual Navigation and Locomotion over obstacles (ViNL), which enables a quadrupedal robot to navigate unseen apartments while stepping over small obstacles that lie in i…
Retrospectives on the Embodied AI Workshop
Matt Deitke, Dhruv Batra, Yonatan Bisk +36
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped i…
Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation
Joanne Truong, Max Rudolph, Naoki Yokoyama +3
If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simu…
Sim-to-Real Transfer for Vision-and-Language Navigation
Peter Anderson, Ayush Shrivastava, Joanne Truong +4
We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions. Recent work o…
Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents
Joanne Truong, Sonia Chernova, Dhruv Batra
Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simul…
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Joanne Truong, Denis Yarats, Tianyu Li +4
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation po…
Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?
Abhishek Kadian, Joanne Truong, Aaron Gokaslan +6
Does progress in simulation translate to progress on robots? If one method outperforms another in simulation, how likely is that trend to hold in reality on a robot? We examine thi…