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[bibtex]@InProceedings{Ehsani_2024_CVPR, author = {Ehsani, Kiana and Gupta, Tanmay and Hendrix, Rose and Salvador, Jordi and Weihs, Luca and Zeng, Kuo-Hao and Singh, Kunal Pratap and Kim, Yejin and Han, Winson and Herrasti, Alvaro and Krishna, Ranjay and Schwenk, Dustin and VanderBilt, Eli and Kembhavi, Aniruddha}, title = {SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {16238-16250} }
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Abstract
Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents. RL requires extensive reward shaping and auxiliary losses and is often too slow and ineffective for long-horizon tasks. While IL with human supervision is effective collecting human trajectories at scale is extremely expensive. In this work we show that imitating shortest-path planners in simulation produces agents that given a language instruction can proficiently navigate explore and manipulate objects in both simulation and in the real world using only RGB sensors (no depth map or GPS coordinates). This surprising result is enabled by our end-to-end transformer-based SPOC architecture powerful visual encoders paired with extensive image augmentation and the dramatic scale and diversity of our training data: millions of frames of shortest-path-expert trajectories collected inside approximately 200000 procedurally generated houses containing 40000 unique 3D assets. Our models data training code and newly proposed 10-task benchmarking suite CHORES are available at https://spoc-robot.github.io/.
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