arXiv subjects

Khuzema Habib

Publications and source records attributed to Khuzema Habib.

4 recordsLinked to original sources

arXiv2026

STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models

Natural-language robot instructions often specify more than a coarse task goal: they may impose spatial, temporal, and logical requirements that must remain satisfied throughout execution. We present STeP, a specification-based agentic framework that uses Signal Temporal Logic (STL) as an explicit interface between high-level language reasoning and low-level robot execution. Rather than encoding such requirements implicitly in a learned policy, STeP formalizes them as task specifications that can be decomposed across multi-stage manipulation, enforced during execution, monitored online, and used as structured feedback for replanning. We evaluate STeP on standard LIBERO and LIBERO-PRO, and introduce LIBERO-Constrained, a new benchmark for manipulation tasks with spatial, temporal, and logical requirements, together with real-world tabletop experiments. On LIBERO-PRO, STeP retains 54%-89% success across five of six evaluated perturbation settings, substantially outperforming VLA and code-as-policy baselines under distribution shift. On LIBERO-Constrained, STeP achieves 80% safe success across 49 task-constraint instances; on real-world tasks, it improves safe success over a specification-free model-based baseline across all four task categories, with gains of up to 45 percentage points. These results support explicit formal specifications as a practical interface between foundation-model reasoning and reliable robot execution.

cs.RO↗

arXiv2026

WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors

Local wind conditions strongly influence drone performance: headwinds increase flight time, crosswinds and wind shear hinder agility in cluttered spaces, while tailwinds reduce travel time. Although adaptive controllers can mitigate turbulence, they remain unaware of the surrounding geometry that generates it, preventing proactive avoidance. Existing methods that model how wind interacts with the environment typically rely on computationally expensive fluid dynamics simulations, limiting real-time adaptation to new environments and conditions. To bridge this gap, we present WESPR, a fast framework that predicts how environmental geometry affects local wind conditions, enabling proactive path planning and control adaptation. Our lightweight pipeline integrates geometric perception and local weather data to estimate wind fields, compute cost-efficient paths, and adjust control strategies, all within 10 seconds. We validate WESPR on a Crazyflie drone navigating turbulent obstacle courses. Our results show a 35.6% reduction on average in maximum trajectory deviation and a 24.6% improvement in stability compared to a wind-agnostic adaptive controller.

cs.RO↗

arXiv2026

AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation

Vision-based robot learning often relies on dense image or point-cloud inputs, which are computationally heavy and entangle irrelevant background features. Existing keypoint-based approaches can focus on manipulation-centric features and be lightweight, but either depend on manual heuristics or task-coupled selection, limiting scalability and semantic understanding. To address this, we propose AFFORD2ACT, an affordance-guided framework that distills a minimal set of semantic 2D keypoints from a text prompt and a single image. AFFORD2ACT follows a three-stage pipeline: affordance filtering, category-level keypoint construction, and transformer-based policy learning with embedded gating to reason about the most relevant keypoints, yielding a compact 38-dimensional state policy that can be trained in 15 minutes, which performs well in real-time without proprioception or dense representations. Across diverse real-world manipulation tasks, AFFORD2ACT consistently improves data efficiency, achieving an 82% success rate on unseen objects, novel categories, backgrounds, and distractors.

cs.RO↗

arXiv2025

Contextual Neural Moving Horizon Estimation for Robust Quadrotor Control in Varying Conditions

Adaptive controllers on quadrotors typically rely on estimation of disturbances to ensure robust trajectory tracking. Estimating disturbances across diverse environmental contexts is challenging due to the inherent variability and uncertainty in the real world. Such estimators require extensive fine-tuning for a specific scenario, which makes them inflexible and brittle to changing conditions. Machine-learning approaches, such as training a neural network to tune the estimator's parameters, are promising. However, collecting data across all possible environmental contexts is impossible. It is also inefficient as the same estimator parameters could work for "nearby" contexts. In this paper, we present a sequential decision making strategy that decides which environmental contexts, using Bayesian Optimization with a Gaussian Process, to collect data from in order to ensure robust performance across a wide range of contexts. Our method, Contextual NeuroMHE, eliminates the need for exhaustive training across all environments while maintaining robust performance under different conditions. By enabling the neural network to adapt its parameters dynamically, our method improves both efficiency and generalization. Experimental results in various real-world settings demonstrate that our approach outperforms the prior work by 20.3\% in terms of maximum absolute position error and can capture the variations in the environment with a few carefully chosen contexts.

cs.RO↗