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Learning Neural Parsers with Deterministic Differentiable Imitation Learning

arXiv 1806.07822 ↗First posted 20 Jun 2018cs.LG cs.AI cs.CVcited by 0

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects into segments as a parsing approach. We make the insight that the derivation of a parse-tree that decomposes the object into segments closely resembles a decision tree constructed by ID3, which can be done when the ground-truth available. We learn to imitate an expert parsing oracle, such that our neural parser can generalize to parse natural images without ground truth. We introduce a novel deterministic policy gradient update, DRAG (i.e., DeteRministically AGgrevate) in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural parser. From another perspective, our approach is a variant of the Deterministic Policy Gradient suitable for the imitation learning setting. The deterministic policy representation offered by training our neural parser with DRAG allows it to outperform state of the art imitation and reinforcement learning approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 41 references that are in the map. First posted on arXiv 20 Jun 2018.

Builds on

10 of 41 references.

References · 10

  1. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
  4. 2014Reinforcement and Imitation Learning via Interactive No-Regret Learningcited by 116
  5. 2017Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Predictioncited by 93
  6. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labellingcited by 37
  7. 2017Learning Heuristic Search via Imitationcited by 34
  8. 2018Fast Policy Learning through Imitation and Reinforcementcited by 26
  9. 2015Deep Hierarchical Parsing for Semantic Segmentationcited by 6
  10. 2017Adaptive Information Gathering via Imitation Learningcited by 6

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  3. 2024Instant Policy: In-Context Imitation Learning via Graph Diffusion
  4. 2026TransMASK: Masked State Representation through Learned Transformation
  5. 2024Learning with Language-Guided State Abstractions
  6. 2021Smooth Imitation Learning via Smooth Costs and Smooth Policies
  7. 2025D2PPO: Diffusion Policy Policy Optimization with Dispersive Loss
  8. 2024Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames