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Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation

Shaocheng Liu, Yuan Meng, Zhenshan Bing, Alois Knoll

arXiv:2610.19141v1cs.RO

TL;DR

Collective Cartesian manipulation learning must account for embodiment-dependent transitions, which can make pooled action modes ambiguous. PACE separates task-context extraction from predictive dynamics adaptation. It reports over 93% overall success and 89% on dynamically out-of-distribution robots, with zero-shot transfer to unseen physical hardware.

  • Problem

    Different embodiments can realize the same Cartesian command through different transitions, creating strategy ambiguity when collective policies average conflicting actuation modes.

    Source: §I · pp. 1–2

  • Method

    PACE combines an offline representation layer that extracts task context with a decision layer using reward and next-step prediction as latent regularizers.

    Source: §I · pp. 1–2

  • Results

    Over 93% overall success and 89% on dynamically out-of-distribution robots; Transformer behavior cloning achieves 73% and diffusion 66%.

    Source: §I · p. 2

  • Takeaways & Limitations

    The evaluations report zero-shot transfer to previously unseen physical hardware.

    Source: §I · p. 2

Abstract

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Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynamics. Collective learning aggregates multi-robot experience to discover shared task structure, yet naive aggregation can average incompatible action modes when different embodiments realize the same Cartesian command through different transition behavior. This leads to strategy ambiguity and performance collapse on dynamically outlying embodiments. To address this, we propose PACE, a two-stage collective learning framework that separates shared task-space intent from embodiment-dependent transition dynamics. The first stage employs a representation layer pre-trained on offline trajectories to extract task-level context from collective data. The second stage introduces a decision layer equipped with a predictive dynamics adapter. By forcing the latent state to predict future transitions, this adapter acts as a latent regularizer that grounds the policy in the transition behavior of the executing robot while preserving real-time inference without online planning. Evaluations across simulated embodiments demonstrate that our method prevents performance collapse on distinct robots. It achieves over 93\% overall success and 89\% on dynamically out-of-distribution zero-shot simulated robots, outperforming the Transformer behavior cloning (82\%) and diffusion policy baselines (66\%). Physical KUKA deployment further validates the feasibility of the full state-based pipeline under zero-shot sim-to-real transfer.

I. INTRODUCTION

Collective Cartesian manipulation policies must share task structure while accounting for embodiment-dependent transitions, which can make pooled behavior ambiguous. PACE separates task-context extraction from predictive dynamics adaptation and reports strong simulated-robot results and zero-shot physical transfer.

I. INTRODUCTION

  • Different embodiments can realize the same Cartesian command through different transitions, so pooled behavior cloning may average conflicting actuation modes into suboptimal actions.The paper motivates separating shared task semantics from hardware-specific physics.Source: §I · p. 1
  • PACE uses an offline-pretrained representation layer to extract embodiment-agnostic task context, followed by a decision layer whose predictive dynamics adapter regularizes latent states.The adapter forces latent states to predict future transitions, implicitly adapting the policy to the executing robot’s physical constraints.Source: §I · pp. 1–2
  • Over 93% overall success and 89% on dynamically out-of-distribution robots; Transformer behavior cloning achieves 73% and diffusion 66%.The reported results are presented as preventing performance collapse on outlier robots.Source: §I · p. 2
  • The contributions frame shared state-based Cartesian manipulation as combining shared task structure with embodiment-dependent transitions that create strategy ambiguity.They also introduce reward prediction for task semantics and auxiliary next-step prediction for adaptation across manipulators.Source: §I · p. 2
  • Evaluations report zero-shot transfer to previously unseen physical hardware, alongside improved robustness on outlier robots.PACE is named Predictive Adaptation for Collective Embodiments Learning.Source: §I · p. 2

II. RELATED WORK

Prior collective and cross-embodiment methods seek reusable manipulation structure, while low-level dynamic discrepancies remain difficult to address. The paper formulates these differences under unified Cartesian state and action spaces and distinguishes latent prediction from online-planning world models.

II. RELATED WORK

  • Prior fleet and cross-embodiment methods align state spaces or use heterogeneous pretrained transformers, but can struggle with low-level dynamic discrepancies across robots.The related work also describes collective data and universal interfaces as routes to generalizable manipulation skills.Source: §II · p. 2
  • Explicit predictive world models can require iterative online simulation and active interaction, whereas PACE uses predictive dynamics as a latent regularizer for real-time adaptation.The paper identifies this distinction as relevant to zero-shot transfer from offline demonstrations.Source: §II · p. 2

A. Problem formulation

  • The formulation uses unified Cartesian workspace states and end-effector actions, while transition dynamics remain robot-dependent under the same commands.It models collective multi-task imitation as MDPs indexed by robot embodiment and task; physical properties and controller-mediated tracking contribute to transition differences.Source: A. Problem formulation · p. 2

B. System overview

The framework separates transferable task structure from embodiment-dependent transition behavior. Its representation layer encodes trajectory context using reconstruction, task contrast, and dynamics prediction objectives.

B. System overview

  • PACE separates shared task context from embodiment-dependent transitions, using representation and decision stages to avoid action averaging in pooled behavior cloning.The representation stage captures shared structure; the decision stage preserves transition behavior needed for control.Source: B. System overview · p. 3

C. Representation Layer: In-context trajectory encoding

  • A Transformer encodes a sliding window of trajectory history into a [CLS] context vector representing robot identity and task nature.Same-task trajectories are positives and different-task trajectories negatives for task disentanglement.Source: C. Representation Layer: In-context… · p. 3
  • Masked trajectory reconstruction trains the encoder to recover randomly masked states and actions from unmasked context.The reconstruction loss compares predictions with ground-truth values at masked indices.Source: C. Representation Layer: In-context… · p. 3
  • A weighted sum of masked reconstruction, task contrastive, and auxiliary dynamics losses trains a representation of task and environment requirements.The dynamics head predicts the next state from the current state, action, and context, encouraging context to capture robot-specific transitions.Source: C. Representation Layer: In-context… · p. 3

D. Decision Layer: Dynamics-aware policy learning

The decision layer grounds latent states in embodiment-specific transitions while retaining task-relevant features. Its predictive model regularizes representation learning, while the policy remains model-free at inference.

D. Decision Layer: Dynamics-aware policy learning

  • The Predictive Dynamics Adapter refines latent states to provide physical grounding beyond the semantic context supplied by the representation layer.The layer is intended to distinguish embodiments such as high-inertia industrial arms and lightweight cobots.Source: D. Decision Layer: Dynamics-aware… · p. 4
  • Next-step prediction error regularizes the latent state to encode robot-specific transitions without using the predictive model for planning or synthetic rollouts.The policy remains model-free during inference, avoiding online planning latency.Source: D. Decision Layer: Dynamics-aware… · p. 4
  • A reward prediction module preserves task-critical semantic features alongside dynamics prediction of embodiment-specific transition behavior.The reward model predicts immediate reward to keep the latent state task-relevant.Source: D. Decision Layer: Dynamics-aware… · p. 4
  • The unified policy conditions on latent state and context, and Gaussian parameter matching distills expert action distributions into the collective policy.The objective matches the student and expert means and log-variances; the text says MSE is used for numerical stability with deterministic experts.Source: D. Decision Layer: Dynamics-aware… · p. 4

IV. EXPERIMENTS

The experiments examine whether dynamics-aware learning improves collective manipulation across embodiments with different transition behavior. They focus on outlier robustness, representation alignment, and data efficiency.

IV. EXPERIMENTS

  • All methods use a shared low-dimensional state representation and Cartesian action interface, focusing comparisons on embodiment-dependent action realization.The evaluation asks whether the latent dynamics regularizer prevents performance degradation on outlier embodiments.Source: §IV · pp. 4–5
  • Behavior cloning performs well on lightweight manipulators but degrades on embodiments with substantial physical discrepancies, including seen outliers and unseen heavy-dynamics robots.The passage identifies difficulty controlling seen outliers with distinct kinematics and limited zero-shot transfer to unseen heavy-dynamics robots.Source: §IV · p. 5
  • The experiments also investigate how learned representations facilitate adaptation and whether transition modeling improves sample efficiency.Source: §IV · p. 5

A. Experimental setup

The evaluation spans simulated manipulation scenarios across multiple robot embodiments and includes a physical KUKA transfer setup. Training uses expert-policy data, with robot-count subsets for scaling analysis.

A. Experimental setup

  • The customized Meta-World benchmark includes 10 manipulation tasks and nine robot meshes, forming 90 task scenarios; the meshes are derived from MuJoCo Menagerie.The tasks are inherited from Meta-World [21].Source: A. Experimental setup · p. 5
  • Sim-to-real validation uses a KUKA LBR iiwa 7 R800 with a Robotiq 2F-85 gripper, camera-based perception, and AprilTags for 3D object positions.A RealSense D455 camera is mounted at the table edge.Source: A. Experimental setup · p. 5
  • Data from 50 expert policies supports offline representation pretraining and online distillation for on-policy fine-tuning.Source: A. Experimental setup · p. 5
  • Scaling subsets use N = {1, 3, 5} robots, and success rate is reported as mean ± standard deviation across task-robot pairs over five seeds.Comparisons include a joint-training Transformer and a Diffusion Policy trained on the same offline datasets.Source: A. Experimental setup · p. 5

B. Robustness to dynamic heterogeneity

PACE maintains high success across robot categories and avoids the heavy-robot collapse seen with Transformer behavior cloning. Robustness depends on dynamic outliers, not simply whether a robot was seen during training.

Overall performance and the heterogeneity gap

  • Over 93% success across robot categories, with PACE outperforming Transformer and diffusion-policy baselines.The per-robot analysis reports over 15 percentage points of improvement on heavy arms, reaching approximately 89% on UR10e and KUKA iiwa.Source: B. Robustness to dynamic heterogeneity · pp. 5–6, Figure 4 · p. 6
  • Seen-versus-unseen status alone does not identify the main challenge; robustness requires handling out-of-distribution dynamics at the training-distribution tails.The section attributes the baseline's heavy-arm degradation to unresolved conflicting kinematic gradients and invalid averaged actions.Source: B. Robustness to dynamic heterogeneity · pp. 5–6

Dynamic outliers

  • Transformer behavior cloning remains near 74% on seen UR10e and 73% on unseen KUKA iiwa, while PACE reaches approximately 89% on both.The binary seen/unseen split masks dynamic divergence: a lightweight unseen robot can be easier to control than a heavy seen robot.Source: B. Robustness to dynamic heterogeneity · pp. 5–6

Breaking the generalization ceiling

  • On held-out KUKA iiwa, adding robot diversity improves PACE success monotonically, while the baseline suffers negative transfer at N = 3.PACE trained on N = 3 robots surpasses the baseline trained on all five; on UR10e, it reaches 80% versus the baseline's 60% with lightweight-dominated N = 3 training.Source: B. Robustness to dynamic heterogeneity · p. 6, Figure 4 · p. 6, Figure 5 · p. 6

C. Analysis of Learned Representation

The learned context embeddings cluster primarily by task rather than robot, consistent with representing shared task intent across embodiments. The analysis also suggests they retain dynamic information useful to the adapter.

Semantic clustering and functional equivalence

Latent dynamics encoding

  • Outlier-embodiment analysis suggests the context retains dynamic signatures that the adapter can use to infer dynamics-related characteristics.The authors connect this interpretation to performance recovery on UR10e.Source: C. Analysis of Learned Representation · p. 6

D. Ablation Studies

The ablations distinguish the roles of the two stages: Stage 1 establishes task intent and supports lightweight-robot performance, while Stage 2 addresses physically distinct outliers. Removing the auxiliary Stage 1 dynamics loss only slightly reduces performance.

The regularization effect of the latent dynamics adapter

  • Without the dynamics adapter, Stage 1 improves success on standard lightweight manipulators from approximately 82% to over 90% versus Transformer behavior cloning.The authors report that extracted task intent resolves the strategy ambiguity that causes baseline failures.Source: D. Ablation Studies · p. 7, Figure 7 · p. 7
  • On UR10e, adding the Stage 2 predictive dynamics adapter raises success from approximately 74% without it to approximately 89% with the full method.Stage 1 alone is insufficient for physically distinct, high-inertia outliers.Source: D. Ablation Studies · p. 7, Figure 7 · p. 7
  • Stage 1 supplies general task strategies, while Stage 2 adapts control to the dynamics of the executing hardware.The authors report that Stage 2 corrects performance drops on outlier equipment.Source: D. Ablation Studies · p. 7

Impact of representation objectives

  • Removing the auxiliary dynamics loss in Stage 1 only slightly reduces performance; Stage 2 can stabilize movements despite imperfect static representations.The representation provides task context to prevent action conflicts, while the Stage 2 adapter corrects physical differences.Source: D. Ablation Studies · p. 7, Table III · p. 7

E. Real world deployment

The trained policy was deployed zero-shot on a physical KUKA iiwa across six manipulation tasks, achieving 79.2% overall success. Hardware discrepancies and contact-rich tasks exposed specific failure modes despite successful transfer without real-world fine-tuning.

Real-world deployment

  • The trained policy was deployed on a physical KUKA LBR iiwa 7 R800 to evaluate practical viability and transferability amid real-world non-idealities, including friction, inertial discrepancies, and control latencies.The evaluation covered Reach, Push, Pick-Place, Button Press, Window Open, and Drawer Open.Source: E. Real world deployment · p. 7
  • Physical gripper mismatch destabilized action execution, while handle/frame contact and drawer tilt caused failures in Window Open and Drawer Open.The drawer's unmodeled downward tilt led the arm to collide with the table and trigger safety limits.Source: E. Real world deployment · p. 7
  • The predictive dynamics regularizer supports zero-shot sim-to-real transfer without real-world fine-tuning.Source: E. Real world deployment · p. 8

V. CONCLUSION

The conclusion presents a two-stage framework that separates task intent from embodiment-specific transition rules, reporting robustness to kinematic outliers and zero-shot KUKA transfer. It also identifies joint-level constraints and limited multi-task testing as boundaries for future work.

  • The two-stage framework combines a task-intent representation layer with a decision layer containing a predictive dynamics adapter, decoupling semantic objectives from physical transition rules.Source: §V · p. 8
  • Predictive regularization prevents performance degradation on kinematic outliers and enables zero-shot transfer to physical hardware such as the KUKA manipulator.Source: §V · p. 8
  • The Cartesian action abstraction prioritizes workspace geometry but bypasses joint-level constraints and kinematic singularities, occasionally causing execution stalls.Source: §V · p. 8
  • Future work should integrate joint-state feedback into the predictive dynamics adapter and broaden quantitative testing across multiple tasks to clarify system boundaries and deployment capabilities.Source: §V · p. 8
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