Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.20241

Learning Navigation from Multimodal Demonstrations with Geometric Candidate Modes

Chengyang Peng·Zhihao Zhang·Upama Pant·Yiming Wang·Keith A. Redmill·Ayonga Hereid
View original source ↗Read source document ↗Export metadata ↓

Abstract

Offline navigation demonstrations are multimodal, because one observation often allows several valid actions, such as passing an obstacle on either side. Behavior cloning averages these actions, potentially leading to unsafe routes. Multimodal policies must discover the alternatives from the data, but similar states can lead to different routes whose current actions overlap. In navigation, the alternatives have a geometric cause and can be computed from one observation. We propose a hierarchical neural-network policy that computes candidate modes from local geometry and learns from demonstrations which mode to follow and how. A high-level policy selects a mode using the goal and its previous choice among a set of candidate modes, and a low-level policy turns it into an action. In simulation, the policy succeeds in over 95\% of seen and unseen test cases, compared with at most 43\% and 20\% for four offline learning baselines and 79\% and 67\% for a heuristic that uses the same modes without learning. The simulation-trained policy also transfers to a LimX Tron 1 bipedal robot.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chengyang Peng, Zhihao Zhang, Upama Pant, Yiming Wang, Keith A. Redmill, Ayonga Hereid. 2026-10-14. Learning Navigation from Multimodal Demonstrations with Geometric Candidate Modes. https://arxiv.org/abs/2610.20241

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation

Efficiently tackling multiple tasks within complex environments, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions such as reinforcement learning (RL). The high cost of data gathering in robotics encourages data-efficient approaches such as model-based RL, which builds a dynamic model of the robot to conduct most policy learning on cheaper simulations, while enabling data reuse and transfer between tasks. In this work, we employ Bayesian deep learning (BDL) with a double purpose. First, we increase the model quality by improving its calibration. Second, we improve data efficiency by performing active learning of the dynamic model during a preliminary exploration phase, actively estimating the information gain of each transition and using it as the exploration reward. We compare several BDL methods in a realistic robot manipulation setup. Our experiments show that our approach matches the quality of relevant alternatives with much lower robot execution requirements and, unlike previous studies validated only on standard RL environments, we tackle robotic arm end-tasks on a complete manipulator simulator such as \texttt{CoppeliaSim}. Code is available at https://github.com/ropertunizar/BayesianActiveExploration.

cs.RO↗
arXiv2026

Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered

Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C constraints. Compression techniques such as quantization, distillation, and pruning are well-established for LLMs and VLMs. Pruning, however, remains particularly underexplored for VLA models. We surprisingly find that pruning VLA models yields drastic performance degradation and increased safety violations. Through spectral analysis, we identify structural properties of VLA weights that help explain this fragility. Based on this insight, we introduce GLUESTICK, a post-pruning recovery method that restores much of the original model's capability while preserving sparsity benefits. GLUESTICK computes a one-time corrective term by interpolating between the dense and pruned models in weight space. At inference, each pruned layer applies this correction to recover lost performance with minimal overhead. Our approach requires no additional training and is agnostic to the pruning algorithm. Across diverse VLA architectures and domains, GLUESTICK recovers up to 100% of the success rate and restores safety to near-dense levels, while saving up to 5 GB of memory and achieving up to 1.75x speedup. Additional material can be found at: https://gluestick-vla.github.io/

cs.RO↗
arXiv2026

Schrödinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation

Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots. Yet methods that perform well in simulation often degrade in cluttered real-world scenes with severe occlusion and latent hazards, where large unseen regions make single-scene inference brittle and unsafe. We propose Schrödinger's Navigator, a belief-aware framework that reasons at inference time over multiple trajectory-conditioned imagined 3D futures. Given candidate paths, a trajectory-conditioned 3D world model predicts hypothetical observations and maintains a superposition of plausible scene realizations rather than committing to one map. An adaptive occluder-aware sampler directs imagination to uncertainty-critical regions, while a Future-Aware Value Map (FAVM) aggregates imagined futures for robust, proactive action selection. Experiments in simulation and on a physical Go2 quadruped show that Schrödinger's Navigator outperforms strong ZSON baselines, improving hidden-target discovery and risk-aware waypoint selection in occlusion-heavy navigation scenarios. These results highlight imagined 3D futures as a scalable and generalizable strategy for zero-shot navigation in uncertain real-world environments.

cs.RO↗