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3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, Huazhe Xu

arXiv:2403.03954v7cs.ROcs.CVcs.LG

TL;DR

Visual imitation learning for complex, generalizable robot skills can require many human demonstrations. DP3 combines compact 3D point-cloud representations with diffusion policies; across 72 simulated tasks it reports a 24.2% relative improvement over its 2D counterpart, alongside real-world generalization and fewer safety violations.

  • Problem

    Visual imitation learning for robust, generalizable skills can require extensive demonstrations, while prior 3D policies face challenges with high-dimensional control and inference speed.

    Source: §I · p. 1, B. Visual Imitation Learning · p. 2

  • Method

    DP3 encodes sparse point clouds into compact 3D features and conditions diffusion-based action generation on those features and robot poses.

    Source: §I · pp. 1–2, B. Perception · p. 3

  • Results

    Across 72 simulated tasks, DP3 outperforms its 2D counterpart by 24.2% relative margin and is reported to generalize broadly and cause fewer real-world safety violations.

    Source: §I · p. 2, §VI · p. 10

  • Takeaways & Limitations

    The paper’s analyses report that simple point clouds are preferred over other intricate 3D representations and better suited to diffusion policies than other policy backbones.

    Source: §I · p. 2

Abstract

from arXiv · show

Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact 3D visual representation, extracted from sparse point clouds with an efficient point encoder. In our experiments involving 72 simulation tasks, DP3 successfully handles most tasks with just 10 demonstrations and surpasses baselines with a 24.2% relative improvement. In 4 real robot tasks, DP3 demonstrates precise control with a high success rate of 85%, given only 40 demonstrations of each task, and shows excellent generalization abilities in diverse aspects, including space, viewpoint, appearance, and instance. Interestingly, in real robot experiments, DP3 rarely violates safety requirements, in contrast to baseline methods which frequently do, necessitating human intervention. Our extensive evaluation highlights the critical importance of 3D representations in real-world robot learning. Videos, code, and data are available on https://3d-diffusion-policy.github.io .

I. INTRODUCTION

DP3 addresses the demonstration burden in visual imitation by conditioning diffusion policies on compact 3D point-cloud features. Its evaluations and analyses report broad generalization with few demonstrations and advantages over other representations and policy backbones.

I. INTRODUCTION

  • Visual imitation learning can require extensive demonstrations; Diffusion Policy is reported to need 100–200 human-collected demonstrations per real-world task.Collecting these demonstrations can take several days, while online learning brings safety, resetting, intervention, and hardware-cost challenges.Source: §I · p. 1
  • DP3 encodes sparse point clouds with an efficient MLP, then uses the compact 3D representation and robot poses to condition diffusion-based action-sequence generation.The paper frames this as combining 3D spatial understanding with diffusion models’ expressiveness.Source: §I · pp. 1–2

A. Diffusion Models in Robotics

  • The paper evaluates DP3 across 72 simulated tasks and four real-world tasks, and identifies 3D conditioning as a novel extension of diffusion policies.Its contributions include real-world deformable-object manipulation with a dexterous hand using 40 demonstrations.Source: §I · p. 2, A. Diffusion Models in Robotics · p. 2

I. INTRODUCTION

  • DP3 combines superior accuracy with fewer demonstrations and training steps, generalization across space, viewpoint, instance, and appearance, and fewer erratic real-world commands than baselines.Its analyses also report that other policy backbones benefit less from 3D representations, while point clouds outperform other 3D modalities and encoders.Source: §I · p. 2

B. Visual Imitation Learning

DP3 is designed to address limitations in visual imitation and prior 3D policies through a general visuomotor policy and a compact point-cloud encoder. A five-demonstration Reach example illustrates its spatial generalization, while the perception design uses single-view point clouds and a lightweight encoder.

B. Visual Imitation Learning

  • Prior 3D policies use keyframe pose extraction for prediction and planning, which is less suitable for high-dimensional control, and their intricate architectures can slow inference.The paper cites PerAct at 2.23 FPS and 3D Diffuser Actor at 1.67 FPS, while noting the latter comparison involves different task settings.Source: B. Visual Imitation Learning · p. 2

C. Learning Dexterous Skills

  • The paper distinguishes its approach from dexterity methods based on retargeting or human videos, arguing that complex skills with minimal demonstrations can be pursued by improving the imitation-learning algorithm itself.DIME and DexMV are cited as examples of the distinct design-centric approaches.Source: C. Learning Dexterous Skills · p. 3

III. METHOD

  • DP3 maps visual observations to actions, using point-cloud perception to produce visual features and a diffusion-policy backbone to generate action sequences conditioned on them.The stated objective is to reproduce demonstrated skills while generalizing beyond training data.Source: §III · p. 3

A. A Motivating Example

  • With five training points in MetaWorld Reach, DP3 reaches points distributed across 3D space; Diffusion Policy and IBC cover a plane-like area, while BCRNN fails to cover the space.The example is presented as evidence of generalization and efficiency when data are limited.Source: A. A Motivating Example · p. 3

B. Perception

  • For practical real-world use, DP3 uses a single-view camera rather than multiple cameras and represents the 3D scene with sparse point clouds.The paper reports point clouds as more efficient than RGB-D, depth, and voxel alternatives in its ablations.Source: B. Perception · p. 3
  • Depth images are converted to point clouds without color channels, cropped to a bounding box, and downsampled with farthest point sampling; 512 or 1,024 points suffice in the tested tasks.The paper says omitting color supports appearance generalization and cropping removes redundant table and ground points.Source: B. Perception · p. 3
  • The DP3 Encoder uses a three-layer MLP, max-pooling, and a projection head to produce a 64-dimensional feature, and is reported to outperform pre-trained point encoders such as PointNeXt.LayerNorm layers are interleaved to stabilize training.Source: B. Perception · p. 3

C. Decision

DP3 conditions diffusion-based action generation on 3D visual features and robot poses. Across 72 simulated tasks, it achieves a 24.2% relative improvement over Diffusion Policy, and a five-demonstration example illustrates broader spatial coverage.

C. Decision

  • The decision module conditions on 3D visual features and robot poses, then iteratively denoises Gaussian noise into a noise-free action sequence.The reverse process performs K denoising iterations, with its coefficients depending on the noise scheduler.Source: C. Decision · p. 3
  • Training samples an action, adds diffusion noise, and trains the denoising network to predict the noise added to the original data.The implementation uses sample prediction, with 100 training timesteps and 10 inference timesteps.Source: C. Decision · pp. 3–4
  • 24.2% relative improvement over Diffusion Policy is reported for DP3 averaged across 72 simulation tasks, with smaller variance.The main-results table gives the aggregate comparison; individual task success rates are in Appendix C.Source: Table I · p. 4
  • Table II compares DP3 with IBC, BCRNN, and their 3D variants, IBC+3D and BCRNN+3D; the 3D variants use the DP3 Encoder for a fair comparison.Source: Table II · p. 4
  • With five demonstrations and 1,000 evaluations in MetaWorld Reach, DP3 covers 3D space, while Diffusion Policy and IBC succeed only in partial space and BC-RNN fails to learn the skill.The figure caption reports successful-trial counts from left to right as 0 / 285 / 327 / 415.Source: Figure 3 · p. 4

IV. SIMULATION EXPERIMENTS

The simulation benchmark spans diverse robotic tasks, domains, and simulators, while using shared demonstrations for fair algorithm comparisons. The experiments compare against image-based diffusion policy and other baselines under a common evaluation protocol.

A. Experiment Setup

  • The benchmark covers 72 tasks across 7 domains, ranging from dexterous and deformable manipulation to parallel-gripper tasks, and uses four simulators.The tasks use MuJoCo, Sapien, IsaacGym, and PlasticineLab; downsampled point clouds visualize selected tasks.Source: A. Experiment Setup · p. 5, Figure 4 · p. 5
  • Demonstrations come from human teleoperation, scripted MetaWorld policies, or domain-specific reinforcement-learning agents, and all imitation algorithms use the same data.VRL3 supplies Adroit trajectories, while PPO supplies trajectories for the other RL-collected domains.Source: A. Experiment Setup · p. 5
  • The primary baseline is image-based Diffusion Policy; additional methods are evaluated on 10 tasks, with matched image and depth resolutions across methods.Each experiment uses 3 seeds; each seed evaluates 20 episodes every 200 epochs, and the reported success rate averages the highest 5 rates.Source: A. Experiment Setup · p. 5

B. Efficiency and Effectiveness

DP3 achieves stronger simulation success rates and learning efficiency than image-based Diffusion Policy, with advantages also reported in demonstration scaling and inference speed. Ablations favor point clouds and the simple DP3 encoder, and describe design choices affecting accuracy, speed, and generalization.

B. Efficiency and Effectiveness

  • Success exceeds 90% on nearly 30 tasks for DP3, versus fewer than 15 for Diffusion Policy; DP3 also has no tasks below 10% success.Most tasks use 10 demonstrations; DP3 typically converges within about 500 epochs, compared with slower or sub-optimal convergence for Diffusion Policy.Source: B. Efficiency and Effectiveness · p. 5
  • DP3 typically converges faster, scales with more demonstrations, and has marginally faster inference than Diffusion Policy.The learning-curve comparison covers 12 tasks; in Adroit, DP3 reaches comparable accuracy with fewer demonstrations, while sufficient demonstrations help on harder MetaWorld tasks.Source: B. Efficiency and Effectiveness · p. 5, Figure 5 · p. 6, Figure 6 · p. 6

C. Ablations

  • Alternative RGB-D, depth, and voxel representations fall short of point clouds, while point clouds and oracle states are competitive.The authors note that RGB-D and depth are close to each other and not comparable to point clouds, emphasizing proper use of depth information.Source: C. Ablations · p. 6, Table IV · p. 6
  • None of the tested complex or pretrained point encoders matches the DP3 Encoder.Comparisons include PointNet, PointNet++, PointNeXt, and Point Transformer.Source: C. Ablations · p. 6, Table V · p. 6
  • Removing PointNet’s T-Net and BatchNorm layers raises its average success rate to 72.3, compared with 78.3 for the DP3 Encoder.The authors suggest fixed camera views may explain why T-Net transformations are unnecessary; reducing feature dimensions from 1024 to 256 changes success from 72.5 to 72.3 while increasing speed.Source: C. Ablations · pp. 6–7, Table VI · p. 7
  • Design choices include cropping point clouds for accuracy, LayerNorm for training stability, sample prediction for faster convergence, and a projection head for faster inference without accuracy loss.Removing color channels supports appearance generalization; DPM-solver++ is competitive with DDIM in low-dimensional control but does not handle high-dimensional control well.Source: C. Ablations · p. 7, Table VII · p. 7

A. Experiment Setup

The real-robot setup evaluates four multistage manipulation tasks using an Allegro hand and a gripper, with camera-based visual observations. Demonstrations vary task objects or positions, and each task uses 40 demonstrations.

A. Experiment Setup

  • The benchmark uses an Allegro hand and a Franka-based gripper across four real-world tasks, with visual observations from one RealSense L515 camera.The tasks are visualized with collected trajectory point clouds.Source: A. Experiment Setup · p. 7, Figure 8 · p. 7
  • The Allegro hand performs Roll-Up, Dumpling, and Drill, while the gripper pours dried meat floss into a bowl.The tasks involve wrapping plasticine, forming dumpling pleats, touching a cube with a drill, and pouring floss from a bowl.Source: A. Experiment Setup · p. 7
  • Demonstrations randomize plasticine shape and object appearance for Roll-Up and Dumpling, and object positions for Drill and Pour.The multi-finger tasks are designed to use capabilities beyond a parallel gripper, including wrapping plasticine without extra tools and grasping a large, heavy drill.Source: A. Experiment Setup · pp. 7–8, Figure 9 · p. 8
  • Human teleoperation supplies 40 demonstrations per task; real-world baselines include image-based and depth-based diffusion policies.The tasks are multistage and involve complex multi-finger robots and deformable objects, making demonstration collection time-consuming.Source: A. Experiment Setup · p. 8

B. Effectiveness

With 40 demonstrations per task, DP3 handles the real-robot benchmark with high success rates, while image- and depth-based policies have lower average accuracy. The reported generalization evaluations cover spatial positions, appearance, object instances, views, and cluttered scenes, with specific scope limits described for view changes.

B. Effectiveness

  • DP3 handles all four real-robot tasks with high success rates, while image- and depth-based diffusion policies have comparatively low average accuracy.The image-based policy excels at Drill but fails entirely at Roll-Up; the depth-based policy reaches 40% success on Roll-Up.Source: B. Effectiveness · p. 8, Table VIII · p. 8

C. Generalization

  • DP3 succeeds in 4 of 5 trials at unseen bowl positions, while baseline methods fail to generalize to all tested positions.The Pour evaluation tests five positions unseen during training, with one trial at each position.Source: C. Generalization · p. 8, Table IX · p. 8
  • DP3 generalizes across cube colors, whereas the baselines do not achieve successful appearance generalization in the reported Drill evaluation.The evaluation trains on a green cube and tests five colors, with one trial per color; depth-based policy accuracy on the training object is lower.Source: C. Generalization · p. 8, Table X · p. 9
  • DP3 manages varied everyday-object instances in Drill, with downsampled point clouds described as reducing confusion across instances.The evaluation replaces the training cube with five differently sized everyday objects, each tested once.Source: C. Generalization · p. 9, Table XI · p. 9
  • DP3 generalizes across minor camera-view variations, but significant view changes may be difficult and require manually transformed point clouds and adjusted cropping.The authors state that accurate point-cloud transformation is not necessary for these minor variations.Source: C. Generalization · p. 9, Table XII · p. 10
  • In cluttered scenes, DP3 solves the pick-and-place task with a high success rate, while DP3 with PointNeXt fails; color-point-cloud input struggles beyond the training cube and objects.The setup uses 50 training demonstrations; the training color receives 10 trials, while each out-of-domain color and object receives one.Source: C. Generalization · pp. 9–10, Table XIII · p. 10

D. Observation on Deployment Safety

The authors report that DP3 rarely triggers safety violations in real-world experiments, unlike image- and depth-based diffusion policies that often require human termination. Safety was assessed primarily qualitatively, with violation rates also counted in the main experiments.

D. Observation on Deployment Safety

  • DP3 rarely violates safety in the real-world experiments, while image- and depth-based diffusion policies often require human termination to protect the robot.The authors describe DP3 as practical and hardware-friendly, while noting that their safety assessment is primarily qualitative.Source: D. Observation on Deployment Safety · p. 10
  • The authors offer direct 3D observation as an intuitive explanation for avoiding collisions, while reserving theoretical analysis of safety for future work.Source: D. Observation on Deployment Safety · p. 10
  • Table XIV reports safety violation rates counted during the main real-world experiments.The caption identifies the measured quantity but supplies no rate values.Source: Table XIV · p. 10, D. Observation on Deployment Safety · p. 10

VI. CONCLUSION

The conclusion presents DP3 as an efficient visual imitation learning algorithm for simulated and real-world robotic tasks using few demonstrations. It reports improved simulation performance and real-world generalization, while identifying representation choice and extremely long horizons as open boundaries.

VI. CONCLUSION

  • Across 72 simulated tasks, DP3 outperforms its 2D counterpart by a relative margin of 24.2%.Source: §VI · p. 10
  • DP3 combines 3D representations with diffusion policies and performs complex real-world manipulation with an Allegro hand, using a small set of demonstrations.Source: §VI · p. 10
  • The conclusion reports robust generalization across aspects and fewer real-world safety violations, but leaves the optimal control representation unknown and extremely long-horizon tasks unexplored.Source: §VI · p. 10

APPENDIX

DP3’s implementation combines downsampled point-cloud and robot-pose representations, then conditions a diffusion decision backbone on their concatenation. The appendix specifies the encoder, action horizons, and normalization used in the implementation.

A. Implementation Details

  • The visual input is a colorless point cloud downsampled by farthest point sampling to 512 or 1024 points, then encoded into a compact representation.The encoder uses pointwise MLP layers, max-pools across points, and projects the result to 64 dimensions.Source: A. Implementation Details · p. 14
  • The implementation uses xyz coordinates, while the encoder also supports xyzrgb inputs.The code sets three input channels for xyz and notes six channels for xyzrgb.Source: A. Implementation Details · p. 14
  • Robot-pose and point-cloud representations are concatenated into a 128-dimensional representation that conditions a diffusion backbone producing action sequences.The model predicts H actions from Nobs observed timesteps and executes the last Nact actions; DP3 and diffusion-based baselines use H = 4, Nobs = 2, and Nact = 3.Source: A. Implementation Details · p. 14
  • The authors report no significant performance difference between short and long prediction horizons for their tasks and choose a shorter horizon for potential real-world disruptions.They independently scale each action and observation dimension to [−1, 1]; action normalization is required for DDPM and DDIM prediction stability because predictions are clipped to that range.Source: A. Implementation Details · p. 14

B. Task Suite

The appendix describes a suite spanning diverse simulated manipulation tasks and four real-world tasks with specified object or position variations. It also reports a shorter horizon as competitive and a simplified DP3 backbone with nearly twice the inference speed.

B. Task Suite

  • The simulation suite covers manipulation tasks from Adroit, Bi-DexHands, DexArt, DexDeform, DexMV, HORA, and MetaWorld; four real-world tasks vary object appearance or positions.The real-world tasks are Roll-Up, Dumpling, Drill, and Pour; plasticine shape and object appearance vary in the first two, while object positions vary in the latter two.Source: B. Task Suite · p. 14

C. More Simulation Experiments

  • The appendix provides per-task simulation results in Table XVIII and expert success rates in Table XIX, with HORA reported as normalized returns rather than success rates.Experts were evaluated over 200 episodes; MetaWorld results include scripted policies, and DexDeform demonstrations use human teleportation with recorded success rates of 100%.Source: C. More Simulation Experiments · p. 15, Table XVIII · p. 16, Table XIX · p. 17
  • DP3 and Diffusion Policy use H = 4, Nact = 3, a shortened prediction horizon competitive with H = 16, Nact = 8 for both methods.The short horizon is designed for generality in complex and real-robot tasks where human intervention changes the environment and the policy needs to switch action immediately.Source: C. More Simulation Experiments · p. 15, Table XV · p. 15

D. Simple DP3

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