What to Do Next? Memorizing skills from Egocentric Instructional Video
- URL: http://arxiv.org/abs/2507.02997v1
- Date: Tue, 01 Jul 2025 22:53:41 GMT
- Title: What to Do Next? Memorizing skills from Egocentric Instructional Video
- Authors: Jing Bi, Chenliang Xu,
- Abstract summary: We present a novel task, interactive action planning, and propose an approach that combines topological affordance memory with transformer architecture.<n>Our experimental results demonstrate that the proposed approach learns meaningful representations, resulting in improved performance and robust when action deviations occur.
- Score: 43.59787683244105
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Learning to perform activities through demonstration requires extracting meaningful information about the environment from observations. In this research, we investigate the challenge of planning high-level goal-oriented actions in a simulation setting from an egocentric perspective. We present a novel task, interactive action planning, and propose an approach that combines topological affordance memory with transformer architecture. The process of memorizing the environment's structure through extracting affordances facilitates selecting appropriate actions based on the context. Moreover, the memory model allows us to detect action deviations while accomplishing specific objectives. To assess the method's versatility, we evaluate it in a realistic interactive simulation environment. Our experimental results demonstrate that the proposed approach learns meaningful representations, resulting in improved performance and robust when action deviations occur.
Related papers
- Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach [83.21177515180564]
We propose a framework that prioritizes natural language understanding and structured reasoning to enhance the agent's global understanding of the environment.<n>Our method outperforms previous approaches, particularly achieving a 44.4% relative improvement in task success rate.
arXiv Detail & Related papers (2025-05-22T09:08:47Z) - Can foundation models actively gather information in interactive environments to test hypotheses? [56.651636971591536]
We introduce a framework in which a model must determine the factors influencing a hidden reward function.<n>We investigate whether approaches such as self- throughput and increased inference time improve information gathering efficiency.
arXiv Detail & Related papers (2024-12-09T12:27:21Z) - Goal-conditioned Offline Planning from Curious Exploration [28.953718733443143]
We consider the challenge of extracting goal-conditioned behavior from the products of unsupervised exploration techniques.
We find that conventional goal-conditioned reinforcement learning approaches for extracting a value function and policy fall short in this difficult offline setting.
In order to mitigate their occurrence, we propose to combine model-based planning over learned value landscapes with a graph-based value aggregation scheme.
arXiv Detail & Related papers (2023-11-28T17:48:18Z) - Robust Visual Imitation Learning with Inverse Dynamics Representations [32.806294517277976]
We develop an inverse dynamics state representation learning objective to align the expert environment and the learning environment.
With the abstract state representation, we design an effective reward function, which thoroughly measures the similarity between behavior data and expert data.
Our approach can achieve a near-expert performance in most environments, and significantly outperforms the state-of-the-art visual IL methods and robust IL methods.
arXiv Detail & Related papers (2023-10-22T11:47:35Z) - Active Sensing with Predictive Coding and Uncertainty Minimization [0.0]
We present an end-to-end procedure for embodied exploration inspired by two biological computations.
We first demonstrate our approach in a maze navigation task and show that it can discover the underlying transition distributions and spatial features of the environment.
We show that our model builds unsupervised representations through exploration that allow it to efficiently categorize visual scenes.
arXiv Detail & Related papers (2023-07-02T21:14:49Z) - Procedure Planning in Instructional Videosvia Contextual Modeling and
Model-based Policy Learning [114.1830997893756]
This work focuses on learning a model to plan goal-directed actions in real-life videos.
We propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning.
arXiv Detail & Related papers (2021-10-05T01:06:53Z) - Landmark Policy Optimization for Object Navigation Task [77.34726150561087]
This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments.
Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal.
We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks.
arXiv Detail & Related papers (2021-09-17T12:28:46Z) - Embodied Visual Active Learning for Semantic Segmentation [33.02424587900808]
We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding.
We develop a battery of agents - both learnt and pre-specified - and with different levels of knowledge of the environment.
We extensively evaluate the proposed models using the Matterport3D simulator and show that a fully learnt method outperforms comparable pre-specified counterparts.
arXiv Detail & Related papers (2020-12-17T11:02:34Z) - Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning [12.76337275628074]
In this work, we propose a variational dynamic model based on the conditional variational inference to model the multimodality andgenerativeity.
We derive an upper bound of the negative log-likelihood of the environmental transition and use such an upper bound as the intrinsic reward for exploration.
Our method outperforms several state-of-the-art environment model-based exploration approaches.
arXiv Detail & Related papers (2020-10-17T09:54:51Z) - Learning intuitive physics and one-shot imitation using
state-action-prediction self-organizing maps [0.0]
Humans learn by exploration and imitation, build causal models of the world, and use both to flexibly solve new tasks.
We suggest a simple but effective unsupervised model which develops such characteristics.
We demonstrate its performance on a set of several related, but different one-shot imitation tasks, which the agent flexibly solves in an active inference style.
arXiv Detail & Related papers (2020-07-03T12:29:11Z) - Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments.
We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
arXiv Detail & Related papers (2020-07-01T17:52:32Z)
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.