Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach
- URL: http://arxiv.org/abs/2503.08306v4
- Date: Tue, 15 Apr 2025 08:24:40 GMT
- Title: Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach
- Authors: Steeven Janny, Hervé Poirier, Leonid Antsfeld, Guillaume Bono, Gianluca Monaci, Boris Chidlovskii, Francesco Giuliari, Alessio Del Bue, Christian Wolf,
- Abstract summary: We present a large-scale experimental study involving numepisodes navigation episodes in a real environment with a physical robot.<n>We analyze the type of reasoning emerging from end-to-end training.<n>We show in a post-hoc analysis that the value function learned by the agent relates to long-term planning.
- Score: 23.52028824411467
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-conditioned behavior, but benchmarks are still dominated by simulation. In this work, we focus on the fine-grained behavior of fast-moving real robots and present a large-scale experimental study involving \numepisodes{} navigation episodes in a real environment with a physical robot, where we analyze the type of reasoning emerging from end-to-end training. In particular, we study the presence of realistic dynamics which the agent learned for open-loop forecasting, and their interplay with sensing. We analyze the way the agent uses latent memory to hold elements of the scene structure and information gathered during exploration. We probe the planning capabilities of the agent, and find in its memory evidence for somewhat precise plans over a limited horizon. Furthermore, we show in a post-hoc analysis that the value function learned by the agent relates to long-term planning. Put together, our experiments paint a new picture on how using tools from computer vision and sequential decision making have led to new capabilities in robotics and control. An interactive tool is available at europe.naverlabs.com/research/publications/reasoning-in-visual-navigation-of-end-to-end-trained-agen ts.
Related papers
- ForesightNav: Learning Scene Imagination for Efficient Exploration [57.49417653636244]
We propose ForesightNav, a novel exploration strategy inspired by human imagination and reasoning.
Our approach equips robotic agents with the capability to predict contextual information, such as occupancy and semantic details, for unexplored regions.
We validate our imagination-based approach using the Structured3D dataset, demonstrating accurate occupancy prediction and superior performance in anticipating unseen scene geometry.
arXiv Detail & Related papers (2025-04-22T17:38:38Z) - Learning Manipulation by Predicting Interaction [85.57297574510507]
We propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction.
The experimental results demonstrate that MPI exhibits remarkable improvement by 10% to 64% compared with previous state-of-the-art in real-world robot platforms.
arXiv Detail & Related papers (2024-06-01T13:28:31Z) - Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
"Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data.
We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
arXiv Detail & Related papers (2024-01-07T19:11:18Z) - AI planning in the imagination: High-level planning on learned abstract
search spaces [68.75684174531962]
We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training.
We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
arXiv Detail & Related papers (2023-08-16T22:47:16Z) - Learning Navigational Visual Representations with Semantic Map
Supervision [85.91625020847358]
We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps.
Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
arXiv Detail & Related papers (2023-07-23T14:01:05Z) - Embodied Agents for Efficient Exploration and Smart Scene Description [47.82947878753809]
We tackle a setting for visual navigation in which an autonomous agent needs to explore and map an unseen indoor environment.
We propose and evaluate an approach that combines recent advances in visual robotic exploration and image captioning on images.
Our approach can generate smart scene descriptions that maximize semantic knowledge of the environment and avoid repetitions.
arXiv Detail & Related papers (2023-01-17T19:28:01Z) - Towards self-attention based visual navigation in the real world [0.0]
Vision guided navigation requires processing complex visual information to inform task-orientated decisions.
Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world.
This is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.
arXiv Detail & Related papers (2022-09-15T04:51:42Z) - What do navigation agents learn about their environment? [39.74076893981299]
We introduce the Interpretability System for Embodied agEnts (iSEE) for Point Goal and Object Goal navigation agents.
We use iSEE to probe the dynamic representations produced by these agents for the presence of information about the agent as well as the environment.
arXiv Detail & Related papers (2022-06-17T01:33:43Z) - An in-depth experimental study of sensor usage and visual reasoning of
robots navigating in real environments [20.105395754497202]
We study the performance and reasoning capacities of real physical agents, trained in simulation and deployed to two different physical environments.
We show, that for the PointGoal task, an agent pre-trained on wide variety of tasks and fine-tuned on a simulated version of the target environment can reach competitive performance without modelling any sim2real transfer.
arXiv Detail & Related papers (2021-11-29T16:27:29Z) - Diagnosing Vision-and-Language Navigation: What Really Matters [61.72935815656582]
Vision-and-language navigation (VLN) is a multimodal task where an agent follows natural language instructions and navigates in visual environments.
Recent studies witness a slow-down in the performance improvements in both indoor and outdoor VLN tasks.
In this work, we conduct a series of diagnostic experiments to unveil agents' focus during navigation.
arXiv Detail & Related papers (2021-03-30T17:59:07Z)
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.