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Conditional Neural Processes for Self-Supervised Goal Discovery in Action Imitation

Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea, Alessandra Sciutti

arXiv:2610.32688v1cs.ROcs.AI

TL;DR

Learning distinct, connected representations of actions without direct supervision is relevant to imitation and later novel-task learning. This paper evaluates CNP-based DMBN and architectural variants, including attention. It reports that attentive layers support robust, task-agnostic action representations where vanilla CNP does not.

  • Problem

    Learning distinct but connected action representations without direct supervision is needed for imitation, and self-supervised representations can ease later learning of novel tasks.

    Source: §I · p. 1

  • Method

    The authors evaluate CNP-based DMBN and architectural variants, including an attention-based model, for self-supervised action representation.

    Source: §I · p. 1, §II · p. 1

  • Results

    Neural Processes form robust task-agnostic action representations with attentive layers, whereas vanilla CNP fails to represent predictive goal information.

    Source: §VI · p. 2

  • Takeaways & Limitations

    AttnDMBN is a promising model for building a Mirror Neuron System-inspired architecture.

    Source: §VI · p. 2

Abstract

from arXiv · show

Inspired by infants' ability to learn novel tasks by imitating others, in this work we evaluate to what extent Conditional Neural Processes can autonomously learn representations of actions that are informative about their underlying goal. We show that the addition of an attention mechanism in the hidden space fosters the emergence of robust goal representations early on in the observation of an action sequence, making it a promising model for a robotic self-supervised general learner.

I. INTRODUCTION

The paper draws on infant imitation to motivate learning distinct but connected action representations without direct supervision. It evaluates whether DMBN exhibits emergent properties associated with predicting actions and their underlying goals.

I. INTRODUCTION

  • The Mirror Neuron System predicts observed action evolution and is associated with inferring others’ mental states.Its forecasting ability is described at multiple abstraction levels, from motor plans to underlying goals.Source: §I · p. 1
  • Infants’ action forecasting is attributed to passive observation and active association of self-actions with sensory feedback.Both modes connect to imitation, which can associate drives, goals, and motions without explicit rewards.Source: §I · p. 1
  • The study evaluates whether DMBN, a multimodal generative robotic control model inspired by the MNS, manifests these emergent properties.The motivation is that self-supervised representations can ease later learning of novel tasks.Source: §I · p. 1

II. ARCHITECTURES

The section compares the original DMBN with three architectural variants to identify components that affect action representation. The variants change the reconstruction objective, time encoding, or attention-based representation.

II. ARCHITECTURES

  • Three DMBN variations are compared with the original to identify components that affect action representation.Source: §II · p. 1
  • DMBN uses a CNP to encode observed instants into an average representative element, then combines it with queried time to generate a reconstructed vision-proprioceptive signal.Source: §II · p. 1
  • The autoencoder replaces the CNP with a fully connected network and trains to reconstruct the observation set rather than predict.Source: §II · p. 1
  • DMBN-PTE projects input and target times with a fully connected network and adds them to encoded modalities instead of using time input channels.Source: §II · p. 1
  • AttnDMBN uses attention and positional time encoding to form the representative element, omits the latent variable, and generates variance at the output.Encoded observations serve as both keys and values.Source: §II · p. 1

III. METHODOLOGY

The methodology tests whether frozen DMBN representations support classifying partially observed motions as moves or grasps. It varies the observed subsequence and reads out action predictions from unimodal encoders and hidden representations.

III. METHODOLOGY

  • Classification heads on frozen unimodal encoders and hidden projections predict whether a partially observed motion is a move or grasp.The data comprise simulated-arm sequences with first-person images and joint angles; the backbone is pretrained and heads are fine-tuned using the same dataset as the original work.Source: §III · p. 1
  • Backbones train with 1–5 random observation times and 1–5 additional time pairs as targets; classification heads train with 10 random observations.Test subsequences contain 1–20 observations from either the start or end of an action.Source: §III · p. 1
  • Fig. 1 depicts the AttnDMBN architecture, with networks sharing weights when they have the same color.Source: Figure 1 · p. 2
  • Fig. 2 plots classification distance against subsequence length and placement, with test-set means, standard deviations, and individual-sequence outputs.Frame and joint curves come from heads on the corresponding backbone encoders; the hidden head follows the CNP layer.Source: Figure 2 · p. 2
  • Softargmax over two head logits yields a Bernoulli distribution that represents confidence in classifying an observed sequence.Source: §III · p. 2

IV. RESULTS

The results distinguish the original DMBN’s goal information at the sequence end from the attention-based model’s post-attention action information. Reconstruction alone and positional time encoding do not yield the reported goal-related information in the compared representations.

IV. RESULTS

  • The autoencoder remains at chance, while original DMBN encodes goal information from final instants but not long-range predictions from the beginning.Positional time encoding further removes goal-related information from the representations.Source: §IV · p. 2
  • The action is unambiguous at the sequence end because object displacement reveals what was performed.Source: §IV · p. 2
  • AttnDMBN’s unimodal encodings lack action information, but it appears in the head output after the attention layer.Source: §IV · p. 2

V. DISCUSSION

AttnDMBN represents action information after attention and can infer goals from early observations, unlike the vanilla DMBN, which cannot predict action from the first instants.

  • AttnDMBN’s descending error curve shows it can infer the goal from observations at the beginning of an action.Source: §V · p. 2
  • DMBN predicts action better than the autoencoder, indicating that prediction supports goal information while reconstruction alone does not.Source: §V · p. 2
  • Vanilla DMBN cannot predict action from the first instants, while DMBN-PTE’s unimodal encodings perform poorly and its post-CNP representation fails to align with goals.The passage attributes DMBN-PTE’s weak unimodal performance to the lack of time information in its input.Source: §V · p. 2

VI. CONCLUSIONS

The evaluation finds that Neural Processes can form robust task-agnostic action representations in self-supervised frameworks. Attentive layers, unlike a vanilla CNP, achieve predictive goal information, making AttnDMBN promising for an MNS-inspired architecture.

  • Neural Processes form robust task-agnostic action representations in self-supervised frameworks.Source: §VI · p. 2
  • Vanilla CNP fails to represent predictive goal information, while attentive layers achieve this ability.Source: §VI · p. 2
  • The way time is queried is impactful, and AttnDMBN is a promising model for an MNS-inspired architecture.Source: §VI · p. 2

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