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RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective

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arxiv 2404.12281 v3 pith:2BBY3AZ3 submitted 2024-04-18 cs.RO

Chenxi Wang , Hongjie Fang , Hao-Shu Fang , Cewu Lu This is my paper · ORCID
classification cs.RO
keywords riseactionsimitationpointpoliciesreal-worldrobotclouds
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Precise robot manipulations require rich spatial information in imitation learning. Image-based policies model object positions from fixed cameras, which are sensitive to camera view changes. Policies utilizing 3D point clouds usually predict keyframes rather than continuous actions, posing difficulty in dynamic and contact-rich scenarios. To utilize 3D perception efficiently, we present RISE, an end-to-end baseline for real-world imitation learning, which predicts continuous actions directly from single-view point clouds. It compresses the point cloud to tokens with a sparse 3D encoder. After adding sparse positional encoding, the tokens are featurized using a transformer. Finally, the features are decoded into robot actions by a diffusion head. Trained with 50 demonstrations for each real-world task, RISE surpasses currently representative 2D and 3D policies by a large margin, showcasing significant advantages in both accuracy and efficiency. Experiments also demonstrate that RISE is more general and robust to environmental change compared with previous baselines. Project website: rise-policy.github.io.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion

    cs.RO 2026-07 conditional novelty 6.0 of 10

    LAG-Fusion composes asynchronous diffusion policies by rebasing delayed guidance into the current action frame and fusing with latency-aware weights, improving contact-rich manipulation performance.

  2. Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks

    cs.RO 2026-03 conditional novelty 6.0 of 10

    Object-centric skill decomposition with retrieve-align-execute achieves 66% average success on three dexterous two-object tasks using 3–4 demonstrations per object.

  3. SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space

    cs.RO 2025-07 conditional novelty 6.0 of 10

    Continuous SE(3) equivariance is embedded in the policy by representing states, actions, and denoising steps in spherical Fourier space, improving generalization to novel 3D arrangements.

  4. SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

    cs.RO 2025-05 conditional novelty 6.0 of 10

    Injecting annealed noise into the observation-encoder latent of a diffusion policy during inference increases rollout diversity and, combined with success- and value-based data selection, improves imitation-learned ro...

  5. CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World

    cs.RO 2025-02 conditional novelty 6.0 of 10

    CordViP achieves strong real-world dexterous manipulation by feeding a diffusion policy with pose-tracked 3D object models and hand point clouds, pretrained on contact maps and arm-hand coordination.

  6. Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Lift3D uses task-aware depth reconstruction and mapped 2D positional embeddings to let pretrained 2D vision transformers act as 3D point-cloud manipulation policies, beating prior methods on average.

  7. FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation

    cs.RO 2024-11 conditional novelty 6.0 of 10

    FoAR uses a future-contact predictor to gate force/torque features into a vision-based imitation policy and adds a reactive nudge, beating vision-only and naive fusion baselines on three real contact-rich tasks.

  8. DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalization

    cs.RO 2025-11 conditional novelty 5.0 of 10

    Fusing RGB and point-cloud inputs with training-time modality dropout plus cross-attention makes a diffusion visuomotor policy markedly more robust to visual and spatial shifts than unimodal or naively fused baselines.

  9. 4D Visual Pre-training for Robot Learning

    cs.CV 2025-08 conditional novelty 5.0 of 10

    A next-frame point-cloud diffusion pre-training method (FVP) improves DP3 and RDT-1B manipulation success rates on the paper's own tasks.

  10. Causal Information Prioritization for Efficient Reinforcement Learning

    cs.AI 2025-02 reject novelty 5.0 of 10

    CIP combines DirectLiNGAM-style causal masks for state-reward and action-reward links with counterfactual data augmentation and an empowerment objective to improve RL sample efficiency.

  11. FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulation

    cs.RO 2024-12 conditional novelty 5.0 of 10

    A consistency flow matching policy conditioned on 3D point clouds generates robot actions in a single inference step, running 7x faster than DP3 with comparable success rates.

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