Right Place, Right Time! Dynamizing Topological Graphs for Embodied Navigation
Embodied Navigation tasks often involve constructing topological graphs of a scene during exploration to facilitate high-level planning and decision-making for execution in continuous environments. Prior literature makes the assumption of static graphs with stationary targets, which does not hold in many real-world environments with moving objects. To address this, we present a novel formulation generalizing navigation to dynamic environments by introducing structured object transitions to dynamize static topological graphs called Object Transition Graphs (OTGs). OTGs simulate portable targets following structured routes inspired by human habits. We apply this technique to Matterport3D (MP3D), a popular simulator for evaluating embodied tasks. On these dynamized OTGs, we establish a navigation benchmark by evaluating Oracle-based, Reinforcement Learning, and Large Language Model (LLM)-based approaches on a multi-object finding task. Further, we quantify agent adaptability, and make key inferences such as agents employing learned decision-making strategies generalize better than those relying on privileged oracle knowledge. To the best of our knowledge, ours is the first work to introduce structured temporal dynamism on topological graphs for studying generalist embodied navigation policies. The code and dataset for our OTGs will be made publicly available to foster research on embodied navigation in dynamic scenes.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingLanguage ModelingLanguage ModellingLarge Language ModelObjectVisual GroundingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Closing the convergence gap of SGD without replacement
Stochastic gradient descent without replacement sampling is widely used in practice for model training. However, the vast majority of SGD analyses assumes data is sampled with replacement, and when the function minimized…
A deterministic and computable Bernstein-von Mises theorem
Bernstein-von Mises results (BvM) establish that the Laplace approximation is asymptotically correct in the large-data limit. However, these results are inappropriate for computational purposes since they only hold over …
Exploring Noncommutative Polynomial Equation Methods for Discrete-Time Quaternionic Control
We present new polynomial-based methods for discrete-time quaternionic systems, highlighting how noncommutative multiplication modifies classical control approaches. Defining quaternionic polynomials via a backward-shift…
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training
To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. T…
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization in the mining industry. Theoretically, de…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)