Papers › RVT: Robotic View Transformer for 3D Object Manipulation

RVT: Robotic View Transformer for 3D Object Manipulation

26 Jun 2023arXiv:2306.14896archive 2025-07-28

Ankit Goyal, Jie Xu, Yijie Guo, Valts Blukis, Yu-Wei Chao, Dieter Fox

For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, we propose RVT, a multi-view transformer for 3D manipulation that is both scalable and accurate. Some key features of RVT are an attention mechanism to aggregate information across views and re-rendering of the camera input from virtual views around the robot workspace. In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than the existing state-of-the-art method (PerAct). It also trains 36X faster than PerAct for achieving the same performance and achieves 2.3X the inference speed of PerAct. Further, RVT can perform a variety of manipulation tasks in the real world with just a few (∼10) demonstrations per task. Visual results, code, and trained model are provided at https://robotic-view-transformer.github.io/.

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NVlabs/RVT officialpytorchNOASSERTION report

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ObjectRobot ManipulationRobot Manipulation Generalization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Robot Manipulation RLBench RVT Inference Speed (fps) 11.6 #10 of 18 Archive leaderboard report
Robot Manipulation RLBench RVT Input Image Size 128 #10 of 18 Archive leaderboard report
Robot Manipulation RLBench RVT Succ. Rate (18 tasks, 100 demo/task) 62.9 #10 of 18 Archive leaderboard report
Robot Manipulation RLBench RVT Training Time (V100 x 8 x day) 1 #10 of 18 Archive leaderboard report

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