Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations

• URL: http://arxiv.org/abs/2603.05576v2
• Date: Mon, 09 Mar 2026 15:29:16 GMT
• Title: Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations
• Abstract summary: Generalizing skill policies to novel conditions remains a key challenge in robot learning.<n>Imitation learning methods, while data-efficient, are largely confined to the training region and consistently fail on input data outside it.<n>We propose a novel joint learning approach to achieve accurate and efficient knowledge transfer.
• Score: 8.01638683277787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalizing skill policies to novel conditions remains a key challenge in robot learning. Imitation learning methods, while data-efficient, are largely confined to the training region and consistently fail on input data outside it, leading to unpredictable policy failures. Alternatively, transfer learning approaches offer methods for trajectory generation robust to both changes in environment or tasks, but they remain data-hungry and lack accuracy in zero-shot generalization. We address these challenges by framing the problem in the context of task inversion learning and proposing a novel joint learning approach to achieve accurate and efficient knowledge transfer. Our method constructs a common representation of the forward and inverse tasks, and leverages auxiliary forward demonstrations from novel configurations to successfully execute the corresponding inverse tasks, without any direct supervision. We show the extrapolation capabilities of our framework via ablation studies and experiments in simulated and real-world environments that require complex manipulation skills with a diverse set of objects and tools, where we outperform diffusion-based alternatives.

Related papers

• End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting [3.398964351541323]
  We propose a novel method for sim-to-real transfer of reinforcement learning policies.<n>We employ a variational autoencoder to jointly learn self-supervised feature representations for style transfer.<n>We demonstrate the application of our approach based on the case study of robot cutting of unknown materials.
  arXiv  Detail & Related papers  (2026-01-28T18:45:55Z)
• FAST: Similarity-based Knowledge Transfer for Efficient Policy Learning [57.4737157531239]
  Transfer Learning offers the potential to accelerate learning by transferring knowledge across tasks.<n>It faces critical challenges such as negative transfer, domain adaptation and inefficiency in selecting solid source policies.<n>In this work we challenge the key issues in TL to improve knowledge transfer, agents performance across tasks and reduce computational costs.
  arXiv  Detail & Related papers  (2025-07-27T22:21:53Z)
• Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models [71.34520793462069]
  Unsupervised reinforcement learning (RL) aims at pre-training agents that can solve a wide range of downstream tasks in complex environments.<n>We introduce a novel algorithm regularizing unsupervised RL towards imitating trajectories from unlabeled behavior datasets.<n>We demonstrate the effectiveness of this new approach in a challenging humanoid control problem.
  arXiv  Detail & Related papers  (2025-04-15T10:41:11Z)
• Training a Generally Curious Agent [77.61142660542599]
  Paprika is a fine-tuning approach that enables language models to develop general decision-making capabilities.<n>Paprika teaches models to explore and adapt their behavior on a new task based on environment feedback in-context without more gradient updates.<n>Results suggest a promising path towards AI systems that can autonomously solve sequential decision-making problems.
  arXiv  Detail & Related papers  (2025-02-24T18:56:58Z)
• Learning Task Representations from In-Context Learning [67.66042137487287]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL)<n>We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads.<n>The proposed method successfully extracts task-specific information from in-context demonstrations and excels in both text and regression tasks.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning [8.860366821983211]
  STRAP is a technique for leveraging pre-trained vision foundation models and dynamic time warping to retrieve sub-sequences of trajectories from large training corpora in a robust fashion.<n>This work proposes STRAP, a technique for leveraging pre-trained vision foundation models and dynamic time warping to retrieve sub-sequences of trajectories from large training corpora in a robust fashion.
  arXiv  Detail & Related papers  (2024-12-19T18:54:06Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies [37.09286945259353]
  We propose an approach to learn abstract motor skills from data using a hierarchical mixture latent variable model. We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours.
  arXiv  Detail & Related papers  (2021-12-09T17:37:14Z)
• Adaptive Policy Transfer in Reinforcement Learning [9.594432031144715]
  We introduce a principled mechanism that can "Adapt-to-Learn", that is adapt the source policy to learn to solve a target task. We show that the presented method learns to seamlessly combine learning from adaptation and exploration and leads to a robust policy transfer algorithm.
  arXiv  Detail & Related papers  (2021-05-10T22:42:03Z)
• Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes [25.513074215377696]
  This paper proposes a continual online model-based reinforcement learning approach. It does not require pre-training to solve task-agnostic problems with unknown task boundaries. In experiments, our approach outperforms alternative methods in non-stationary tasks.
  arXiv  Detail & Related papers  (2020-06-19T23:52:45Z)
• Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling [126.69933134648541]
  We present a meta-reinforcement learning algorithm that is both efficient and extrapolates well when faced with out-of-distribution tasks at test time. Our method is based on a simple insight: we recognize that dynamics models can be adapted efficiently and consistently with off-policy data.
  arXiv  Detail & Related papers  (2020-06-12T13:34:46Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.