Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems

Path planning for high-speed unmanned surface vehicles requires more complex solutions to reduce sailing time and save energy. This article proposes a new predictive artificial potential field that incorporates time information and predictive potential to plan smoother paths. It explores the principles of the artificial potential field, considering vehicle dynamics and local minimum reachability. The study first analyzes the most advanced traditional artificial potential field and its drawbacks in global and local path planning. It then introduces three modifications to the predictive artificial potential field-angle limit, velocity adjustment, and predictive potential to enhance the feasibility and flatness of the generated path. A comparison between the traditional and predictive artificial potential fields demonstrates that the latter successfully restricts the maximum turning angle, shortens sailing time, and intelligently avoids obstacles. Simulation results further verify that the predictive artificial potential field addresses the concave local minimum problem and improves reachability in special scenarios, ultimately generating a more efficient path that reduces sailing time and conserves energy for unmanned surface vehicles.

UEREBot: Learning Safe Quadrupedal Locomotion under Unstructured Environments and High-Speed Dynamic Obstacles

Quadruped robots are increasingly deployed in unstructured environments. Safe locomotion in these settings requires long-horizon goal progress, passability over uneven terrain and static constraints, and collision avoidance against high-speed dynamic obstacles. A single system cannot fully satisfy all three objectives simultaneously: planning-based decisions can be too slow, while purely reactive decisions can sacrifice goal progress and passability. To resolve this conflict, we propose UEREBot (Unstructured-Environment Reflexive Evasion Robot), a hierarchical framework that separates slow planning from instantaneous reflexive evasion and coordinates them during execution. UEREBot formulates the task as a constrained optimal control problem blueprint. It adopts a spatial--temporal planner that provides reference guidance toward the goal and threat signals. It then uses a threat-aware handoff to fuse navigation and reflex actions into a nominal command, and a control barrier function shield as a final execution safeguard. We evaluate UEREBot in Isaac Lab simulation and deploy it on a Unitree Go2 quadruped equipped with onboard perception. Across diverse environments with complex static structure and high-speed dynamic obstacles, UEREBot achieves higher avoidance success and more stable locomotion while maintaining goal progress than representative baselines, demonstrating improved safety--progress trade-offs.

A Low-Cost Vision-Based Tactile Gripper with Pretraining Learning for Contact-Rich Manipulation

Robotic manipulation in contact-rich environments remains challenging, particularly when relying on conventional tactile sensors that suffer from limited sensing range, reliability, and cost-effectiveness. In this work, we present LVTG, a low-cost visuo-tactile gripper designed for stable, robust, and efficient physical interaction. Unlike existing visuo-tactile sensors, LVTG enables more effective and stable grasping of larger and heavier everyday objects, thanks to its enhanced tactile sensing area and greater opening angle. Its surface skin is made of highly wear-resistant material, significantly improving durability and extending operational lifespan. The integration of vision and tactile feedback allows LVTG to provide rich, high-fidelity sensory data, facilitating reliable perception during complex manipulation tasks. Furthermore, LVTG features a modular design that supports rapid maintenance and replacement. To effectively fuse vision and touch, We adopt a CLIP-inspired contrastive learning objective to align tactile embeddings with their corresponding visual observations, enabling a shared cross-modal representation space for visuo-tactile perception. This alignment improves the performance of an Action Chunking Transformer (ACT) policy in contact-rich manipulation, leading to more efficient data collection and more effective policy learning. Compared to the original ACT method, the proposed LVTG with pretraining achieves significantly higher success rates in manipulation tasks.

Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies

Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size and demonstrations. We introduce Skill Mixture-of-Experts Policy (SMP), a diffusion-based mixture-of-experts policy that learns a compact orthogonal skill basis and uses sticky routing to compose actions from a small, task-relevant subset of experts at each step. A variational training objective supports this design, and adaptive expert activation at inference yields fast sampling without oversized backbones. We validate SMP in simulation and on a real dual-arm platform with multi-task learning and transfer learning tasks, where SMP achieves higher success rates and markedly lower inference cost than large diffusion baselines. These results indicate a practical path toward scalable, transferable multi-task manipulation: learn reusable skills once, activate only what is needed, and adapt quickly when tasks change.

CoFreeVLA: Collision-Free Dual-Arm Manipulation via Vision-Language-Action Model and Risk Estimation

Vision Language Action (VLA) models enable instruction following manipulation, yet dualarm deployment remains unsafe due to under modeled selfcollisions between arms and grasped objects. We introduce CoFreeVLA, which augments an endtoend VLA with a short horizon selfcollision risk estimator that predicts collision likelihood from proprioception, visual embeddings, and planned actions. The estimator gates risky commands, recovers to safe states via risk-guided adjustments, and shapes policy refinement for safer rollouts. It is pre-trained with model-based collision labels and posttrained on real robot rollouts for calibration. On five bimanual tasks with the PiPER robot arm, CoFreeVLA reduces selfcollisions and improves success rates versus RDT and APEX.

Soft Responsive Materials Enhance Humanoid Safety

Humanoid robots are envisioned as general-purpose platforms in human-centered environments, yet their deployment is limited by vulnerability to falls and the risks posed by rigid metal-plastic structures to people and surroundings. We introduce a soft-rigid co-design framework that leverages non-Newtonian fluid-based soft responsive materials to enhance humanoid safety. The material remains compliant during normal interaction but rapidly stiffens under impact, absorbing and dissipating fall-induced forces. Physics-based simulations guide protector placement and thickness and enable learning of active fall policies. Applied to a 42 kg life-size humanoid, the protector markedly reduces peak impact and allows repeated falls without hardware damage, including drops from 3 m and tumbles down long staircases. Across diverse scenarios, the approach improves robot robustness and environmental safety. By uniting responsive materials, structural co-design, and learning-based control, this work advances interact-safe, industry-ready humanoid robots.

Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation

In visuomotor policy learning, diffusion-based imitation learning has become widely adopted for its ability to capture diverse behaviors. However, approaches built on ordinary and stochastic denoising processes struggle to jointly achieve fast sampling and strong multi-modality. To address these challenges, we propose the Hybrid Consistency Policy (HCP). HCP runs a short stochastic prefix up to an adaptive switch time, and then applies a one-step consistency jump to produce the final action. To align this one-jump generation, HCP performs time-varying consistency distillation that combines a trajectory-consistency objective to keep neighboring predictions coherent and a denoising-matching objective to improve local fidelity. In both simulation and on a real robot, HCP with 25 SDE steps plus one jump approaches the 80-step DDPM teacher in accuracy and mode coverage while significantly reducing latency. These results show that multi-modality does not require slow inference, and a switch time decouples mode retention from speed. It yields a practical accuracy efficiency trade-off for robot policies.

REBot: Reflexive Evasion Robot for Instantaneous Dynamic Obstacle Avoidance

Dynamic obstacle avoidance (DOA) is critical for quadrupedal robots operating in environments with moving obstacles or humans. Existing approaches typically rely on navigation-based trajectory replanning, which assumes sufficient reaction time and leading to fails when obstacles approach rapidly. In such scenarios, quadrupedal robots require reflexive evasion capabilities to perform instantaneous, low-latency maneuvers. This paper introduces Reflexive Evasion Robot (REBot), a control framework that enables quadrupedal robots to achieve real-time reflexive obstacle avoidance. REBot integrates an avoidance policy and a recovery policy within a finite-state machine. With carefully designed learning curricula and by incorporating regularization and adaptive rewards, REBot achieves robust evasion and rapid stabilization in instantaneous DOA tasks. We validate REBot through extensive simulations and real-world experiments, demonstrating notable improvements in avoidance success rates, energy efficiency, and robustness to fast-moving obstacles. Videos and appendix are available on https://rebot-2025.github.io/.

VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback

Tactile feedback is generally recognized to be crucial for effective interaction with the physical world. However, state-of-the-art Vision-Language-Action (VLA) models lack the ability to interpret and use tactile signals, limiting their effectiveness in contact-rich tasks. Incorporating tactile feedback into these systems is challenging due to the absence of large multi-modal datasets. We present VLA-Touch, an approach that enhances generalist robot policies with tactile sensing \emph{without fine-tuning} the base VLA. Our method introduces two key innovations: (1) a pipeline that leverages a pretrained tactile-language model that provides semantic tactile feedback for high-level task planning, and (2) a diffusion-based controller that refines VLA-generated actions with tactile signals for contact-rich manipulation. Through real-world experiments, we demonstrate that our dual-level integration of tactile feedback improves task planning efficiency while enhancing execution precision. Code is open-sourced at \href{https://github.com/jxbi1010/VLA-Touch}{this URL}.

ArtGS:3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects

Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this work, we introduce ArtGS, a novel framework that extends 3D Gaussian Splatting (3DGS) by integrating visual-physical modeling for articulated object understanding and interaction. ArtGS begins with multi-view RGB-D reconstruction, followed by reasoning with a vision-language model (VLM) to extract semantic and structural information, particularly the articulated bones. Through dynamic, differentiable 3DGS-based rendering, ArtGS optimizes the parameters of the articulated bones, ensuring physically consistent motion constraints and enhancing the manipulation policy. By leveraging dynamic Gaussian splatting, cross-embodiment adaptability, and closed-loop optimization, ArtGS establishes a new framework for efficient, scalable, and generalizable articulated object modeling and manipulation. Experiments conducted in both simulation and real-world environments demonstrate that ArtGS significantly outperforms previous methods in joint estimation accuracy and manipulation success rates across a variety of articulated objects. Additional images and videos are available on the project website: https://sites.google.com/view/artgs/home