arXiv subjects

Qinbo Sun

Publications and source records attributed to Qinbo Sun.

5 recordsLinked to original sources

arXiv2026

Explicit Stair Geometry Conditioning for Robust Humanoid Locomotion

Robust humanoid stair climbing remains challenging due to geometric discontinuities, sensitivity to step height variations, and perception uncertainty in real-world environments. Existing learning-based locomotion policies often rely on implicit terrain representations or blind proprioceptive feedback, limiting their ability to generalize across varying stair geometries and to anticipate required gait adjustments. This paper proposes an explicit stair geometry conditioning framework for robust humanoid stair climbing. Instead of encoding terrain as high-dimensional latent features, we extract a compact set of interpretable geometric parameters, including step height, step depth, and current yaw angle relative to the robot heading. These explicit stair parameters directly condition a Proximal Policy Optimization (PPO)-based locomotion policy, enabling proactive modulation of swing-foot clearance and stride characteristics according to stair structure. Simulation experiments demonstrate improved generalization across unseen stair heights beyond the training distribution. Real-world experiments on the Unitree G1 humanoid validate reliable indoor and outdoor stair traversal. In challenging outdoor scenarios, the robot successfully ascends 33 consecutive steps without failure, demonstrating robustness and practical deployability.

cs.RO↗

arXiv2026

UNCLE-Grasp: A Task-Adapted Framework for Uncertainty-Aware Grasping of Leaf-Occluded Strawberries

Robotic strawberry harvesting remains challenging under partial occlusion, where leaves obscure fruit geometry and make grasp decisions based on a single shape estimate unreliable. A partial observation may admit multiple plausible 3D completions, so a grasp feasible on one completion may fail on another. Existing uncertainty-aware grasping methods estimate uncertainty in pose, shape, or individual candidates, but do not aggregate grasp feasibility across completion hypotheses for an object-level attempt-or-abstain decision. We present UNCLE-Grasp, a task-adapted framework integrating learned shape completion, dropout-based variability estimation, physically grounded grasp evaluation, and risk-aware target-level decisions for leaf-occluded strawberries. Monte Carlo dropout generates multiple completion samples. For each sample, retained grasp candidates are combined into a wrench space to compute a completion-level force-closure score. Variability in this score across plausible completions, rather than geometric point variability alone, quantifies target-level grasp uncertainty. A conservative lower confidence bound determines whether to grasp or abstain. We evaluate the framework in simulation and on a physical robot under increasing synthetic and real leaf occlusion. At the highest simulated occlusion, UNCLE-Grasp raises success among attempted grasps from 0.780 for the strongest completed baseline to 0.870, with similar attempt rates of 0.860 and 0.880, respectively, and raises overall success from 0.680 to 0.740. On the physical robot at approximately 87% synthetic occlusion, it achieves 0.800 success among attempted grasps versus 0.483 for the strongest completed baseline, although at a lower attempt rate. These results show that UNCLE-Grasp enables more reliable risk-aware execution through selective abstention, with a trade-off between grasp reliability and harvesting yield.

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arXiv2025

GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Model

Vision-Language-Action (VLA) models often fail to generalize to unseen camera viewpoints, a limitation stemming from their difficulty in inferring robust 3D geometry from 2D images. We introduce GeoAware-VLA, a simple yet effective approach that enhances viewpoint invariance by integrating strong geometric priors into the vision backbone. Instead of training a visual encoder or relying on explicit 3D data, we leverage a frozen, pretrained geometric vision model as a feature extractor. A lightweight, trainable projection layer then adapts these geometrically-rich features for the policy decoder, relieving it of the burden of learning 3D consistency from scratch. Through extensive evaluations on the LIBERO and CALVIN benchmarks, we show that GeoAware-VLA preserves and even improves in-distribution performance while achieving substantial gains in zero-shot generalization to unseen camera poses, improving unseen-view success rates by an average of 35 percentage points on LIBERO and over 11 percentage points on CALVIN compared to their respective baselines. Crucially, these gains transfer to the physical world, where our model shows significant improvement on a real robotic platform. Our approach proves effective across both continuous and discrete action spaces, highlighting that robust geometric grounding is a key ingredient for building more generalizable robotic agents.

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arXiv2025

SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided Distillation

Efficient path planning in robotics, particularly within large-scale, complex environments, remains a significant hurdle. While Large Language Models (LLMs) offer strong reasoning capabilities, their high computational cost and limited adaptability hinder real-time deployment on edge devices. We present SmallPlan - a novel framework leveraging LLMs as teacher models to train lightweight Small Language Models (SLMs) for high-level path planning tasks. In SmallPlan, the SLMs provide optimal action sequences to navigate across scene graphs that compactly represent full-scaled 3D scenes. The SLMs are trained in a simulation-powered, interleaved manner with LLM-guided supervised fine-tuning (SFT) and reinforcement learning (RL). This strategy not only enables SLMs to successfully complete navigation tasks but also makes them aware of important factors like distance travel, providing more efficient path planning. Through experiments, we demonstrate that the fine-tuned SLMs perform competitively with larger models like GPT-4o on sequential path planning, without suffering from hallucination and overfitting. SmallPlan is resource-efficient, making it well-suited for edge-device deployment and advancing practical autonomous robotics. Our source code is available here: https://github.com/quangpham2006/SmallPlan

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arXiv2018

Energy Optimization of Automatic Hybrid Sailboat

Autonomous Surface Vehicles (ASVs) provide an effective way to actualize applications such as environment monitoring, search and rescue, and scientific researches. However, the conventional ASVs depends overly on the stored energy. Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an auxiliary propulsion system. The electric energy cost of Hybrid Sailboat needs to be optimized to achieve the ocean automatic cruise mission. Based on adjusted setting on sails and rudders, this paper seeks the optimal trajectory for autonomic cruising to reduce the energy cost by changing the heading angle of sailing upwind. The experiment results validate the heading angle accounts for energy cost and the trajectory with the best heading angle saves up to 23.7% than other conditions. Furthermore, the energy-time line can be used to predict the energy cost for long-time sailing.

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