Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations

• URL: http://arxiv.org/abs/2512.21586v1
• Date: Thu, 25 Dec 2025 09:11:14 GMT
• Title: Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations
• Authors: Xin Liu, Haoran Li, Dongbin Zhao,
• Abstract summary: We propose Behavior Cloning from Videos via Latent Representations (BCV-LR) to achieve imitation learning from videos.<n>BCV-LR extracts action-related latent features from high-dimensional video inputs through self-supervised tasks.<n>We conduct extensive experiments on a set of challenging visual tasks, including both discrete control and continuous control.
• Score: 22.561305437484975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can efficiently extract knowledge and learn skills from the videos within only a few trials and errors. However, it poses a big challenge to replicate this learning process for autonomous agents, due to the complexity of visual input, the absence of action or reward signals, and the limitations of interaction steps. In this paper, we propose a novel, unsupervised, and sample-efficient framework to achieve imitation learning from videos (ILV), named Behavior Cloning from Videos via Latent Representations (BCV-LR). BCV-LR extracts action-related latent features from high-dimensional video inputs through self-supervised tasks, and then leverages a dynamics-based unsupervised objective to predict latent actions between consecutive frames. The pre-trained latent actions are fine-tuned and efficiently aligned to the real action space online (with collected interactions) for policy behavior cloning. The cloned policy in turn enriches the agent experience for further latent action finetuning, resulting in an iterative policy improvement that is highly sample-efficient. We conduct extensive experiments on a set of challenging visual tasks, including both discrete control and continuous control. BCV-LR enables effective (even expert-level on some tasks) policy performance with only a few interactions, surpassing state-of-the-art ILV baselines and reinforcement learning methods (provided with environmental rewards) in terms of sample efficiency across 24/28 tasks. To the best of our knowledge, this work for the first time demonstrates that videos can support extremely sample-efficient visual policy learning, without the need to access any other expert supervision.

Related papers

• MVR: Multi-view Video Reward Shaping for Reinforcement Learning [17.20077949643041]
  Multi-View Video Reward Shaping (MVR) is a framework that models the relevance of states regarding the target task using videos captured from multiple viewpoints.<n>MVR learns a state relevance function that mitigates the bias towards specific static poses inherent in image-based methods.<n>We introduce a state-dependent reward shaping formulation that integrates task-specific rewards and VLM-based guidance.
  arXiv  Detail & Related papers  (2026-03-02T10:24:04Z)
• Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation [36.17444261325021]
  Visual Language Navigation (VLN) is a fundamental task within the field of Embodied AI, focusing on the ability of agents to navigate complex environments based on natural language instructions.<n>Existing methods rely on pre-trained backbone models for visual perception, which struggle with the dynamic viewpoints in VLN scenarios.<n>We propose Weakly-supervised Partial Contrastive Learning (WPCL), a method that enhances an agent's ability to identify objects from dynamic viewpoints in VLN scenarios without requiring VLM fine-tuning.
  arXiv  Detail & Related papers  (2025-06-18T11:43:50Z)
• Subtask-Aware Visual Reward Learning from Segmented Demonstrations [97.80917991633248]
  This paper introduces REDS: REward learning from Demonstration with Demonstrations, a novel reward learning framework.<n>We train a dense reward function conditioned on video segments and their corresponding subtasks to ensure alignment with ground-truth reward signals.<n>Our experiments show that REDS significantly outperforms baseline methods on complex robotic manipulation tasks in Meta-World.
  arXiv  Detail & Related papers  (2025-02-28T01:25:37Z)
• Sample-efficient Unsupervised Policy Cloning from Ensemble Self-supervised Labeled Videos [7.827978803804189]
  Unsupervised Policy from Ensemble Self-supervised labeled Videos (SV) is a novel framework to efficiently learn policies from action-free videos without rewards and any other supervision.<n>SV trains a video labeling model to infer the expert actions in expert videos through several combined self-supervised tasks.<n>After a sample-efficient, unsupervised, and iterative training process, SV obtains an advanced policy based on a robust video labeling model.
  arXiv  Detail & Related papers  (2024-12-14T10:12:22Z)
• Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning [9.240917262195046]
  C-LAIfO is a computationally efficient algorithm designed for imitation learning from videos. We analyze the problem of imitation from expert videos with visual discrepancies. Our algorithm performs imitation entirely within this space using off-policy adversarial imitation learning.
  arXiv  Detail & Related papers  (2024-06-18T20:56:18Z)
• PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control [55.81022882408587]
  Temporal action abstractions, along with belief state representations, are a powerful knowledge sharing mechanism for sequential decision making. We propose a novel view that treats inducing temporal action abstractions as a sequence compression problem. We introduce an approach that combines continuous action quantization with byte pair encoding to learn powerful action abstractions.
  arXiv  Detail & Related papers  (2024-02-16T04:55:09Z)
• PALM: Predicting Actions through Language Models [74.10147822693791]
  We introduce PALM, an approach that tackles the task of long-term action anticipation. Our method incorporates an action recognition model to track previous action sequences and a vision-language model to articulate relevant environmental details. Our experimental results demonstrate that PALM surpasses the state-of-the-art methods in the task of long-term action anticipation.
  arXiv  Detail & Related papers  (2023-11-29T02:17:27Z)
• TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning [73.53576440536682]
  We introduce TACO: Temporal Action-driven Contrastive Learning, a powerful temporal contrastive learning approach. TACO simultaneously learns a state and an action representation by optimizing the mutual information between representations of current states. For online RL, TACO achieves 40% performance boost after one million environment interaction steps.
  arXiv  Detail & Related papers  (2023-06-22T22:21:53Z)
• Imitation from Observation With Bootstrapped Contrastive Learning [12.048166025000976]
  Imitation from observation (IfO) is a learning paradigm that consists of training autonomous agents in a Markov Decision Process. We present BootIfOL, an IfO algorithm that aims to learn a reward function that takes an agent trajectory and compares it to an expert. We evaluate our approach on a variety of control tasks showing that we can train effective policies using a limited number of demonstrative trajectories.
  arXiv  Detail & Related papers  (2023-02-13T17:32:17Z)
• Action-Conditioned Contrastive Policy Pretraining [39.13710045468429]
  Deep visuomotor policy learning achieves promising results in control tasks such as robotic manipulation and autonomous driving. It requires a huge number of online interactions with the training environment, which limits its real-world application. In this work, we aim to pretrain policy representations for driving tasks using hours-long uncurated YouTube videos.
  arXiv  Detail & Related papers  (2022-04-05T17:58:22Z)
• Visual Adversarial Imitation Learning using Variational Models [60.69745540036375]
  Reward function specification remains a major impediment for learning behaviors through deep reinforcement learning. Visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents. We develop a variational model-based adversarial imitation learning algorithm.
  arXiv  Detail & Related papers  (2021-07-16T00:15:18Z)
• Self-supervised Video Object Segmentation [76.83567326586162]
  The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking) We make the following contributions: (i) we propose to improve the existing self-supervised approach, with a simple, yet more effective memory mechanism for long-term correspondence matching; (ii) by augmenting the self-supervised approach with an online adaptation module, our method successfully alleviates tracker drifts caused by spatial-temporal discontinuity; (iv) we demonstrate state-of-the-art results among the self-supervised approaches on DAVIS-2017 and YouTube
  arXiv  Detail & Related papers  (2020-06-22T17:55:59Z)

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.