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Learning Foresight without Explicit Trajectories for 3D Diffusion Policies

Zhongbo Zhang, Zaibin Zhang, Yifan Wang, Changbo Yan, Lijun Wang, Huchuan Lu

arXiv:2609.20669v1cs.ROcs.CV

TL;DR

3D diffusion policies generate geometrically grounded actions from current observations, but manipulation also requires anticipating how interactions are unfolding. Movement Trend Guidance learns a future-oriented latent from observation history and consistently improves on DP3, reaching 62.8% vs. 56.1% in 50-task RoboTwin2.0 mixed training.

  • Problem

    3D diffusion policies generate dense actions from geometric observations, but manipulation also requires anticipating how interactions unfold and what they are progressing toward.

    Source: §I · p. 1

  • Method

    Movement Trend Guidance learns a compact latent from short observation histories, supervises it with sparse future gripper states, and retains it as action-policy conditioning at inference.

    Source: §I · p. 1

  • Results

    Performance improves consistently over DP3 across RoboTwin2.0, LIBERO-40, and five real-robot tasks, reaching 62.8% vs. 56.1% in 50-task RoboTwin2.0 mixed training.

    Source: §I · p. 2

  • Takeaways & Limitations

    The trend latent supplies future information without fixing a waypoint or tracking target, leaving local motion correction to use current geometry.

    Source: §VI · p. 8

Abstract

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3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also anticipating where the interaction is heading. Existing policies largely leave such foresight to emerge implicitly from action learning. We introduce Movement Trend Guidance, a simple but effective way to provide this foresight without introducing an explicit plan. From a short observation history, the policy learns a compact latent representation of interaction evolution. During training, sparse future gripper states supervise this representation; at inference, only the latent is retained as future-oriented conditioning alongside the current observation. The latent provides global conditioning for action generation, while an additional gated FiLM branch is used only at the UNet bottleneck. Despite adding only 3.52% more parameters to DP3, our method preserves the original dense-action and receding-horizon formulation and consistently improves upon DP3 across RoboTwin2.0, LIBERO-40, and DexArt. It reaches 62.8% vs. 56.1% in 50-task RoboTwin2.0 mixed training, 71.93% vs. 37.08% on LIBERO-40, and 72.0% vs. 49.0% on five real-robot tasks. These results show that a diffusion policy can benefit substantially from knowing where an interaction is heading, without being told exactly where to move.

I. INTRODUCTION … B. 3D Imitation Learning Policies

Movement Trend Guidance uses sparse future gripper-state supervision to teach a compact latent representation of interaction evolution, then retains that latent as conditioning for dense action generation at inference. It improves on DP3 across the reported simulated and real-robot benchmarks without requiring an explicit planning target.

I. INTRODUCTION

  • Current geometry indicates what action is appropriate now, while successful multi-step manipulation also requires knowing where the interaction is heading, a role standard diffusion policies do not explicitly separate.The anticipated structure can include the next contact or a configuration toward which multiple effectors coordinate.Source: §I · p. 1
  • Waypoints, keyframes [4], [5], and trajectories specify where to move, but prediction errors can constrain later behavior despite new observations that suggest correction.Movement Trend Guidance instead uses future prediction to teach future-relevant representation rather than prescribe motion to execute.Source: §I · pp. 1–2
  • Movement Trend Guidance predicts a compact latent from short point-cloud and robot-state histories, trains it to recover sparse future gripper states, and retains only the latent at inference.The future states supervise what the latent represents; the decoded states are not used as inference-time targets.Source: §I · p. 1
  • The latent uses the standard global-conditioning pathway, with additional gated FiLM modulation restricted to the compressed UNet bottleneck, preserving dense action prediction and receding-horizon execution.The design adds no separate planning module.Source: §I · pp. 1–2
  • 62.8% vs. 56.1% on 50-task RoboTwin2.0 mixed training, 71.93% vs. 37.08% on LIBERO-40, and 72.0% vs. 49.0% on five real-robot tasks.The comparisons are against the matched DP3 baseline. Controlled studies find that future supervision improves the learned latent beyond added representation capacity alone, while directly conditioning on explicit future points is less effective.Source: §I · p. 2

A. Robot Manipulation

  • Sequence and action-chunking policies such as ACT [7], BeT, and VQ-BeT [9] model extended-horizon actions, while vision-language-action models extend policy learning to multiple tasks [10], [11], [12],.Diffusion Policy [14] captures multimodal action distributions and generates coherent action chunks; future interaction structure in these approaches typically emerges implicitly from action learning.Source: A. Robot Manipulation · p. 2

B. 3D Imitation Learning Policies

  • Prior 3D imitation methods use voxelized observations, neural fields, multiview features, or adaptive-resolution representations,, [22],, [23], while DP3 [1] uses point clouds for direct action diffusion.Other methods make intermediate structure explicit through keyframe pose prediction or prediction-and-planning, whereas this work follows DP3’s point-cloud diffusion formulation and adds sparse future-state supervision for a compact movement-trend latent.Source: B. 3D Imitation Learning Policies · p. 2

C. Future-Guided Policy Learning … A. Problem Formulation

Movement Trend Guidance gives 3D diffusion policies future-oriented conditioning through a compact latent learned from observation history, while retaining dense action generation without an explicit decoded trajectory.

C. Future-Guided Policy Learning

  • Rather than model future views or explicit trajectories, the method uses sparse future task-space gripper states for training supervision and retains only the movement-trend latent at inference.The decoded future states are never supplied to the action policy.Source: C. Future-Guided Policy Learning · p. 2

D. Conditional Modulation

  • The trend latent enters the global-conditioning pathway, with gated FiLM modulation restricted to the UNet bottleneck.This lets future-supervised information influence action-sequence structure while preserving observation-driven local refinement.Source: D. Conditional Modulation · p. 2

III. METHOD

  • A hidden latent from observation history compresses future interaction information and provides soft geometric guidance without constraining flexible dense-action generation to a decoded trajectory.Source: §III · p. 2

A. Problem Formulation

  • The policy uses a length-N observation window of point clouds and robot states, predicts a length-H action sequence, and in the stated receding-horizon setting uses N = 3, H = 8, and executes 6 actions before re-observing.Source: A. Problem Formulation · pp. 2–3
  • A lightweight encoder maps the observation window to a compact trend latent, whose auxiliary decoder provides future gripper-target supervision during training but is discarded at inference.The retained latent is a compressed movement trend, not future coordinates.Source: A. Problem Formulation · p. 3, Figure 2 · p. 3
  • The latent summarizes interaction evolution, while the point-cloud encoder and action denoiser remain responsible for scene geometry and local control.Source: A. Problem Formulation · p. 3

B. Latent Movement-Trend Representation

The movement-trend latent provides soft geometric guidance for inferring local motions from current observations, rather than specifying waypoints or a point-by-point tracking trajectory. It is learned from temporally encoded observations and robot states, then shared as conditioning for action generation.

B. Latent Movement-Trend Representation

  • A two-hidden-layer MLP processes temporally concatenated point-cloud and robot-state features to produce the trend latent.The hidden width is 256; each frame is encoded with a DP3-style point-cloud and state encoder.Source: B. Latent Movement-Trend Representation · p. 3
  • For RoboTwin2.0, DP3 and Ours share the same learned 64-dimensional task embedding, keeping task conditioning matched in controlled ablations.Ours additionally uses the movement-trend pathway and bottleneck modulation.Source: B. Latent Movement-Trend Representation · p. 3
  • The trend latent acts as soft geometric guidance, which the policy combines with current point-cloud and robot-state observations to infer and execute local motions.The latent is taken from the hidden layer before an auxiliary future gripper-target head, which supplies a compact training signal rather than a point-by-point tracking trajectory.Source: B. Latent Movement-Trend Representation · p. 3

C. Action Diffusion with Bottleneck-Gated Guidance

The latent conditions the denoising network through its standard global-condition pathway, with an additional gated FiLM branch that modulates only the UNet bottleneck. The scale and bias projections start at zero, making this branch initially an identity mapping.

C. Action Diffusion with Bottleneck-Gated Guidance

  • The latent uses standard global conditioning, while an additional FiLM branch is confined to the UNet bottleneck.Down-sampling and up-sampling blocks retain the ordinary latent-conditioned pathway.Source: C. Action Diffusion with… · p. 4
  • Conditioning from diffusion time and observation produces scale, bias, and a sigmoid gate that blends the FiLM-modulated feature with the original bottleneck feature.The scale and bias transform x as (1 + α) ⊙ x + β, and the output is x + γ ⊙ (˜x − x).Source: C. Action Diffusion with… · p. 4
  • Zero-initialized scale and bias projections make the added branch an exact identity initially, while a negative gate bias further limits modulation as the projections learn.The gated branch is restricted to the bottleneck; down-sampling and up-sampling retain ordinary latent conditioning.Source: C. Action Diffusion with… · p. 4

D. End-to-End Training and Inference

The section describes end-to-end training of a latent representation from sparse future gripper targets, then inference that uses the latent during receding-horizon replanning.

D. End-to-End Training and Inference

  • A single end-to-end objective jointly trains the trend encoder, latent predictor and embedding, observation encoder, and action diffusion model with an auxiliary decoder predicting K=4 future gripper targets.The decoder outputs 4 × 2 × 4 = 32 scalars as a compact geometric training signal, not a waypoint sequence or tracking target. Each target contains Cartesian position (x, y, z) and one gripper state; the slots represent left and right grippers in bimanual tasks, while LIBERO duplicates the single-arm target. Targets use each benchmark’s stored Cartesian frame and enter the loss without separate future-target normalization; RoboTwin offsets refer to original demonstration frames, LIBERO offsets to stride-4 materialized trajectories, and offsets beyond a demonstration are clipped to its final frame.Source: D. End-to-End Training and Inference · p. 4
  • At inference, the policy denoises Gaussian noise under c_t, executes an action subsequence, then refreshes z_t from a new calibrated point cloud and replans without converting the latent into a command or tracking path.The latent is refreshed every receding-horizon cycle.Source: D. End-to-End Training and Inference · p. 4

IV. EXPERIMENTS · A. Experimental Setup · B. Main Results on RoboTwin2.0

Across simulation benchmarks and five real-robot tasks, movement-trend-guided DP3 is evaluated for manipulation performance and deployment cost. On RoboTwin2.0’s 50-task mixed-training setting, it outperforms the matched DP3 variant while adding modest parameters and latency.

IV. EXPERIMENTS

  • The evaluation spans RoboTwin2.0, LIBERO-40, DexArt, and five SO101 real-robot tasks.LIBERO-40 uses 50 demonstrations per task, stride-4 subsampling, 1000 training epochs, and 50 episodes per task across three rollout seeds; DexArt uses 100 demonstrations per task and reports the best-five-checkpoint average, while each real-robot task uses 50 demonstrations and 20 rollouts.Source: §IV · p. 4, A. Experimental Setup · p. 4

A. Experimental Setup

  • RoboTwin2.0 evaluation includes the official setting and joint training across all 50 tasks, using 50 demonstrations and 100 evaluation episodes per task.Mixed training uses 50 demonstrations per task, retains every fourth frame as a window anchor, and trains for 3000 epochs under the official evaluation protocol.Source: A. Experimental Setup · p. 4, Table I · p. 5
  • The method has 271.67M parameters versus DP3’s 262.43M (+3.52%) and raises mean inference latency from 50.28 ms to 50.90 ms (+1.23%).Latency is measured on an NVIDIA RTX 4090 with batch size 1 and 10 DDIM denoising steps.Source: A. Experimental Setup · p. 4

B. Main Results on RoboTwin2.0

  • 62.8% success rate is a +6.7-point gain over DP3* in RoboTwin2.0 mixed training, with improvements on 38 of 50 tasks and broadly comparable results on the rest.Both starred policies use the same learned 64-dimensional task embedding; Ours* additionally uses the trend pathway and bottleneck modulation.Source: Table I · p. 5, B. Main Results on RoboTwin2.0 · p. 5
  • The largest task-level gains are +37 on rotate_QRcode, +21 on move_playingcard_away, +22 on move_pillbottle_pad, and +26 on place_shoe.The paper describes these as contact-rich and multi-stage tasks.Source: B. Main Results on RoboTwin2.0 · p. 5

C. Evaluation Across Environments … D. Real-Robot Experiments

Movement Trend Guidance improves over DP3 on LIBERO-40 and DexArt, and achieves higher average success than DP3 and SimpleDP3 on five real-robot tasks. The evaluations cover two simulated environments and a real dual-arm platform.

C. Evaluation Across Environments

1) LIBERO-40 Point-Cloud Evaluation:

  • LIBERO-40 compares both policies on the same 40 tasks, with 50 training demonstrations per task, calibrated point-cloud preprocessing, and shared evaluation initialization.Each checkpoint from epochs 100 to 1000 is evaluated with seeds 7, 17, and 27 and 50 episodes per task and seed, totaling 6,000 episodes per checkpoint; Table II reports epoch-1000 results and Figure 3 tracks checkpoint success rates.Source: 1) LIBERO-40 Point-Cloud Evaluation · p. 5, Figure 3 · p. 5
  • Figure 3 reports overall LIBERO-40 success across checkpoints saved every 100 epochs from epoch 100 to 1000.The narrow seed deviations in the epoch-1000 results indicate that the advantage is consistent across three evaluation seeds rather than driven by one rollout stream.Source: Figure 3 · p. 5, 1) LIBERO-40 Point-Cloud Evaluation · p. 5

D. Real-Robot Experiments

  • Real-robot tests use point clouds reconstructed from Intel RealSense RGB-D observations for five SO101 dual-arm manipulation tasks.The tasks are push cube, stack bowls, stack cubes, lift basket, and handover bottle; synchronized stereo depth is deprojected using calibrated camera intrinsics and transformed into the robot frame with camera-to-robot extrinsics.Source: D. Real-Robot Experiments · pp. 5–6

1) LIBERO-40 Point-Cloud Evaluation:

  • 71.93% ± 0.49% overall on LIBERO-40 at epoch 1000, our method exceeds DP3 by 34.85 points across the 40 tasks.The result is mean ± sample standard deviation across rollout seeds 7, 17, and 27; the advantage is consistent across seeds, with the largest absolute gain on LIBERO-Goal.Source: 1) LIBERO-40 Point-Cloud Evaluation · p. 5, Table II · p. 6

2) DexArt:

  • 59.25% average success on four DexArt tasks, improving over DP3 by 7.25 points and achieving the highest average among compared methods.DexArt evaluates dexterous articulated-object manipulation across a robot morphology, task structure, and object categories different from RoboTwin2.0.Source: 2) DexArt · p. 5, Table III · p. 6

D. Real-Robot Experiments

E. Failure Cases … B. LIBERO-40 Guidance Ablations

The failure analysis attributes limited gains to occluded, incomplete point clouds, while LIBERO-40 ablations show the complete future-supervised model performs best.

E. Failure Cases

  • Arm occlusion can leave incomplete point clouds that limit trend guidance during precise contact, grasp alignment, or placement; broader validation beyond evaluated SO101 remains future work.Partial observations can perturb the movement-trend estimate, which provides soft guidance alongside current point-cloud and robot-state observations.Source: E. Failure Cases · p. 6

V. ABLATION STUDIES

  • RoboTwin2.0 ablations use a shared policy trained jointly across all 50 tasks, with the main experiment’s sampling, demonstration budget, and evaluation protocol unless stated otherwise.Figure 4 varies guidance construction, future-target offsets, and bottleneck-gated injection scope while keeping observation-history length fixed.Source: §V · p. 6, Figure 4 · p. 7

A. Guidance Construction and Training

  • The guidance ablations test whether bottleneck-gated FiLM adds benefit beyond early fusion, and whether results depend on the MLP predictor or input normalization.The comparisons hold joint training over all 50 RoboTwin2.0 tasks fixed; parameter-matched variants use a diffusion predictor or remove normalization for trend-predictor inputs.Source: A. Guidance Construction and Training · p. 6

B. LIBERO-40 Guidance Ablations

  • LIBERO-40 ablations use a fixed point-cloud protocol, 50 demonstrations per task, stride-4 temporal subsampling, and 1000 training epochs.Epoch-1000 evaluation uses rollout seeds 7, 17, and 27, with 50 episodes per task and seed.Source: B. LIBERO-40 Guidance Ablations · p. 6
  • 71.93 ± 0.49% four-suite success rate: the complete model achieves the highest LIBERO-40 ablation result and exceeds explicit future-point conditioning by +6.65 points.Without future supervision, the parameter-matched latent variant reaches 44.87 ± 0.33%, only +7.79 points above DP3; adding supervision yields a further +27.06 points.Source: Table V · p. 7, B. LIBERO-40 Guidance Ablations · p. 7

C. Future-Target Offset Configuration … VI. CONCLUSION

Movement Trend Guidance adds compact future-oriented conditioning to 3D diffusion policies without prescribing an explicit plan. The reported results favor bottleneck-only gated FiLM and show gains in a six-task ACT validation.

C. Future-Target Offset Configuration

  • The default future-target offsets are {5, 10, 15, 20}, balancing near-term contact relevance with longer-horizon movement direction.The targets supervise the latent during training; executed actions are not required to reach them.Source: C. Future-Target Offset Configuration · p. 7

D. Bottleneck-Gated FiLM Injection

  • 62.8% success with bottleneck-only gated FiLM exceeds 55.8% with all-block FiLM and 52.9% with cross-attention in the 50-task mixed-training comparison.All variants use the same 64-dimensional task embedding; the full method also carries latent-conditioned global representations through the network.Source: D. Bottleneck-Gated FiLM Injection · p. 7

E. Transfer Across Policy Architectures

  • 51.67% mean success with movement-trend conditioning exceeds 24.00% without it across six ACT tasks trained for 10,000 steps.The result is limited to this evaluated ACT setting and does not establish that the gain holds for every architecture.Source: E. Transfer Across Policy Architectures · p. 7

VI. CONCLUSION

  • The method extracts a compact trend latent from short point-cloud and robot-state histories, using it as soft global conditioning rather than decoding it into waypoints or tracking targets.This preserves the action policy’s freedom to correct local motion using current geometry.Source: §VI · p. 8
  • The trend encoder and action diffusion policy are optimized jointly with end-to-end action diffusion training and an auxiliary future gripper-target signal.The conclusion notes a parameter-matched ablation that removes the auxiliary signal while retaining the same latent architecture.Source: §VI · p. 8
  • Experiments cover RoboTwin2.0, LIBERO-40, DexArt, and five SO101 real-robot tasks, with calibrated point clouds obtained from Intel RealSense RGB-D observations.The paper presents the compact trend as a lightweight way to add foresight without turning future information into a hard plan.Source: §VI · p. 8

APPENDIX I FULL PER-TASK RESULTS ON ROBOTWIN2.0 · APPENDIX II EXPERIMENTAL DETAILS AND ADDITIONAL · APPENDIX III COMPLETE LIBERO-40 CHECKPOINT RESULTS

The appendices provide full task-level RoboTwin2.0 results, additional experimental details and analysis, and complete LIBERO-40 checkpoint statistics. They report larger gains on several lower-performing RoboTwin2.0 task groups and describe the evaluation protocols behind the results.

APPENDIX I FULL PER-TASK RESULTS ON ROBOTWIN2.0

  • Table VII reports per-task success rates for 50 RoboTwin2.0 tasks, including category and overall averages, with starred columns showing the 50-task mixed-training results.The starred comparison is between DP3* and Ours*; other columns retain official benchmark comparisons.Source: Appendix I · p. 9, Table VII · p. 10

APPENDIX II EXPERIMENTAL DETAILS AND ADDITIONAL

  • The RoboTwin2.0 comparison uses the same learned 64-dimensional task embedding for both starred policies; Ours* adds trend guidance and bottleneck modulation to DP3*.The trend encoder and action policy are jointly optimized; the latent predictor has a 256-dimensional hidden representation, and both use AdamW with a 1.0 × 10^-4 learning rate.Source: APPENDIX II EXPERIMENTAL DETAILS AND… · p. 9, Table VII · p. 10
  • RoboTwin2.0 evaluations use 50 expert demonstrations per task and 100 episodes; DexArt uses 100 demonstrations and reports the average of its best five evaluation checkpoints.DexArt policies are trained for 3,000 epochs and evaluated every 200 epochs over 20 episodes; this differs from the fixed epoch-1000 LIBERO report.Source: APPENDIX II EXPERIMENTAL DETAILS AND… · p. 9
  • Category gains are largest for multi-object stacking (+10.5 points), followed by handover (+8.5), single-arm handling (+7.9), articulated interaction (+7.6), pick-and-transport (+7.3), precise placement (+5.5), and tool use (+2.6).The passage associates smaller gains on precise placement and tool use with their dependence on fine-grained local geometry.Source: APPENDIX II EXPERIMENTAL DETAILS AND… · p. 9
  • Real-robot evaluation used the SO101 dual-arm platform across five tasks, with 50 demonstrations and 20 rollouts per task.RGB-D camera depth was deprojected using calibrated intrinsics and transformed with camera-to-robot extrinsics to form the policy point cloud.Source: APPENDIX II EXPERIMENTAL DETAILS AND… · p. 9, Figure 5 · p. 11
  • Across DP3* success-rate bands, gains average +6.1 points (<30%, 10/11 improved), +12.0 (30–<40%), +9.8 (40–80%), and 0.0 (>80%).The 30–<40% group contains five tasks; the 40–80% group contains 21, and the >80% group contains 13.Source: APPENDIX II EXPERIMENTAL DETAILS AND… · p. 9
  • The analysis suggests movement trend guidance helps most when a base policy has a reasonable action prior but lacks geometric cues about future movement.The passage contrasts complementary gains at intermediate DP3* success rates with a 0.0-point average change above 80%, which it attributes to a near-ceiling effect.Source: APPENDIX II EXPERIMENTAL DETAILS AND… · pp. 9–11

APPENDIX III COMPLETE LIBERO-40 CHECKPOINT RESULTS

  • Tables VIII and IX report every saved LIBERO-40 checkpoint as suite-level success rate (mean ± sample standard deviation) across rollout seeds 7, 17, and 27.Each suite has 10 tasks and 1,500 evaluation episodes per checkpoint; Overall covers 40 tasks and 6,000 episodes.Source: APPENDIX III COMPLETE LIBERO-40… · p. 11

APPENDIX IV DETAILED ABLATION ANALYSIS · APPENDIX V FULL PER-TASK ABLATION RESULTS ON ROBOTWIN2.0

The detailed ablation favors bottleneck-only gated FiLM over broader or stronger guidance injection, while the appendix tables provide per-task slices of the RoboTwin2.0 variants. The analysis also describes how predictor, preprocessing, and future-offset choices are evaluated.

APPENDIX IV DETAILED ABLATION ANALYSIS

  • The construction and training ablations remove gated FiLM, swap the MLP trend predictor for a diffusion predictor, or remove input normalization.Removing gated FiLM leaves a DP3-style policy with the latent available only through early fusion; parameter-matched variants test predictor and preprocessing choices.Source: APPENDIX IV DETAILED ABLATION ANALYSIS · p. 11
  • End-to-end training lets the latent adapt to the action objective while remaining a soft condition that does not prescribe executed motion.This preserves the distinction between future-oriented conditioning and an explicit motion target.Source: APPENDIX IV DETAILED ABLATION ANALYSIS · p. 12
  • With history fixed at three frames, the selected offsets summarize near-term contact and longer-range movement tendency without setting an intermediate target.Short-range offsets can duplicate information in the observation window, whereas long-range offsets provide broader movement information.Source: APPENDIX IV DETAILED ABLATION ANALYSIS · p. 12
  • 62.8% for Ours* exceeds 55.8% with all-block gated FiLM and 52.9% with cross-attention, which falls below DP3* at 56.1%.The authors interpret this ordering as support for conservative, bottleneck-only guidance injection.Source: APPENDIX IV DETAILED ABLATION ANALYSIS · p. 12
  • All-block gated FiLM may interfere with local action refinement, while cross-attention couples denoising features more strongly to guidance tokens than gated FiLM.Gated FiLM uses element-wise affine modulation with a learnable gate initialized near identity; the authors favor shaping global action structure without dominating local denoising.Source: APPENDIX IV DETAILED ABLATION ANALYSIS · p. 12

APPENDIX V FULL PER-TASK ABLATION RESULTS ON ROBOTWIN2.0

  • Tables X–XII report per-task slices of the 50-task RoboTwin2.0 mixed-training ablations for guidance construction, future-target offsets, and injection.Their columns are variants of a shared policy trained jointly on the full suite; the supplied passages give table scope but no per-task cell values.Source: Table X · p. 14, Table XI · p. 15, Appendix V · pp. 15–16
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