Learning Dual-Arm Push and Grasp Synergy in Dense Clutter

Robotic grasping in densely cluttered environments is challenging due to scarce collision-free grasp affordances. Non-prehensile actions can increase feasible grasps in cluttered environments, but most research focuses on single-arm rather than dual-arm manipulation. Policies from single-arm systems fail to fully leverage the advantages of dual-arm coordination. We propose a target-oriented hierarchical deep reinforcement learning (DRL) framework that learns dual-arm push-grasp synergy for grasping objects to enhance dexterous manipulation in dense clutter. Our framework maps visual observations to actions via a pre-trained deep learning backbone and a novel CNN-based DRL model, trained with Proximal Policy Optimization (PPO), to develop a dual-arm push-grasp strategy. The backbone enhances feature mapping in densely cluttered environments. A novel fuzzy-based reward function is introduced to accelerate efficient strategy learning. Our system is developed and trained in Isaac Gym and then tested in simulations and on a real robot. Experimental results show that our framework effectively maps visual data to dual push-grasp motions, enabling the dual-arm system to grasp target objects in complex environments. Compared to other methods, our approach generates 6-DoF grasp candidates and enables dual-arm push actions, mimicking human behavior. Results show that our method efficiently completes tasks in densely cluttered environments. https://sites.google.com/view/pg4da/home

ManiSkill-ViTac 2025: Challenge on Manipulation Skill Learning With Vision and Tactile Sensing

This article introduces the ManiSkill-ViTac Challenge 2025, which focuses on learning contact-rich manipulation skills using both tactile and visual sensing. Expanding upon the 2024 challenge, ManiSkill-ViTac 2025 includes 3 independent tracks: tactile manipulation, tactile-vision fusion manipulation, and tactile sensor structure design. The challenge aims to push the boundaries of robotic manipulation skills, emphasizing the integration of tactile and visual data to enhance performance in complex, real-world tasks. Participants will be evaluated using standardized metrics across both simulated and real-world environments, spurring innovations in sensor design and significantly advancing the field of vision-tactile fusion in robotics.

VLN-Game: Vision-Language Equilibrium Search for Zero-Shot Semantic Navigation

Following human instructions to explore and search for a specified target in an unfamiliar environment is a crucial skill for mobile service robots. Most of the previous works on object goal navigation have typically focused on a single input modality as the target, which may lead to limited consideration of language descriptions containing detailed attributes and spatial relationships. To address this limitation, we propose VLN-Game, a novel zero-shot framework for visual target navigation that can process object names and descriptive language targets effectively. To be more precise, our approach constructs a 3D object-centric spatial map by integrating pre-trained visual-language features with a 3D reconstruction of the physical environment. Then, the framework identifies the most promising areas to explore in search of potential target candidates. A game-theoretic vision language model is employed to determine which target best matches the given language description. Experiments conducted on the Habitat-Matterport 3D (HM3D) dataset demonstrate that the proposed framework achieves state-of-the-art performance in both object goal navigation and language-based navigation tasks. Moreover, we show that VLN-Game can be easily deployed on real-world robots. The success of VLN-Game highlights the promising potential of using game-theoretic methods with compact vision-language models to advance decision-making capabilities in robotic systems. The supplementary video and code can be accessed via the following link: https://sites.google.com/view/vln-game.

PANav: Toward Privacy-Aware Robot Navigation via Vision-Language Models

Navigating robots discreetly in human work environments while considering the possible privacy implications of robotic tasks presents significant challenges. Such scenarios are increasingly common, for instance, when robots transport sensitive objects that demand high levels of privacy in spaces crowded with human activities. While extensive research has been conducted on robotic path planning and social awareness, current robotic systems still lack the functionality of privacy-aware navigation in public environments. To address this, we propose a new framework for mobile robot navigation that leverages vision-language models to incorporate privacy awareness into adaptive path planning. Specifically, all potential paths from the starting point to the destination are generated using the A* algorithm. Concurrently, the vision-language model is used to infer the optimal path for privacy-awareness, given the environmental layout and the navigational instruction. This approach aims to minimize the robot's exposure to human activities and preserve the privacy of the robot and its surroundings. Experimental results on the S3DIS dataset demonstrate that our framework significantly enhances mobile robots' privacy awareness of navigation in human-shared public environments. Furthermore, we demonstrate the practical applicability of our framework by successfully navigating a robotic platform through real-world office environments. The supplementary video and code can be accessed via the following link: https://sites.google.com/view/privacy-aware-nav.

HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environments

Robot grasping, whether handling isolated objects, cluttered items, or stacked objects, plays a critical role in industrial and service applications. However, current visual grasp detection methods based on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) struggle to adapt across various grasping scenarios due to the imbalance between local and global feature extraction. In this paper, we propose a novel hybrid Mamba-Transformer approach to address these challenges. Our method improves robotic visual grasping by effectively capturing both global and local information through the integration of Vision Mamba and parallel convolutional-transformer blocks. This hybrid architecture significantly improves adaptability, precision, and flexibility across various robotic tasks. To ensure a fair evaluation, we conducted extensive experiments on the Cornell, Jacquard, and OCID-Grasp datasets, ranging from simple to complex scenarios. Additionally, we performed both simulated and real-world robotic experiments. The results demonstrate that our method not only surpasses state-of-the-art techniques on standard grasping datasets but also delivers strong performance in both simulation and real-world robot applications.

Single-Shot 6DoF Pose and 3D Size Estimation for Robotic Strawberry Harvesting

In this study, we introduce a deep-learning approach for determining both the 6DoF pose and 3D size of strawberries, aiming to significantly augment robotic harvesting efficiency. Our model was trained on a synthetic strawberry dataset, which is automatically generated within the Ignition Gazebo simulator, with a specific focus on the inherent symmetry exhibited by strawberries. By leveraging domain randomization techniques, the model demonstrated exceptional performance, achieving an 84.77\% average precision (AP) of 3D Intersection over Union (IoU) scores on the simulated dataset. Empirical evaluations, conducted by testing our model on real-world datasets, underscored the model's viability for real-world strawberry harvesting scenarios, even though its training was based on synthetic data. The model also exhibited robust occlusion handling abilities, maintaining accurate detection capabilities even when strawberries were obscured by other strawberries or foliage. Additionally, the model showcased remarkably swift inference speeds, reaching up to 60 frames per second (FPS).

VITAL: Visual Teleoperation to Enhance Robot Learning through Human-in-the-Loop Corrections

Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions. Despite its potential, the data collection process for IL remains a significant challenge due to the logistical difficulties and high costs associated with obtaining high-quality demonstrations. To address these issues, we propose a low-cost visual teleoperation system for bimanual manipulation tasks, called VITAL. Our approach leverages affordable hardware and visual processing techniques to collect demonstrations, which are then augmented to create extensive training datasets for imitation learning. We enhance the generalizability and robustness of the learned policies by utilizing both real and simulated environments and human-in-the-loop corrections. We evaluated our method through several rounds of experiments in simulated and real-robot settings, focusing on tasks of varying complexity, including bottle collecting, stacking objects, and hammering. Our experimental results validate the effectiveness of our approach in learning robust robot policies from simulated data, significantly improved by human-in-the-loop corrections and real-world data integration. Additionally, we demonstrate the framework's capability to generalize to new tasks, such as setting a drink tray, showcasing its adaptability and potential for handling a wide range of real-world bimanual manipulation tasks. A video of the experiments can be found at: https://youtu.be/YeVAMRqRe64?si=R179xDlEGc7nPu8i

3D Feature Distillation with Object-Centric Priors

Grounding natural language to the physical world is a ubiquitous topic with a wide range of applications in computer vision and robotics. Recently, 2D vision-language models such as CLIP have been widely popularized, due to their impressive capabilities for open-vocabulary grounding in 2D images. Recent works aim to elevate 2D CLIP features to 3D via feature distillation, but either learn neural fields that are scene-specific and hence lack generalization, or focus on indoor room scan data that require access to multiple camera views, which is not practical in robot manipulation scenarios. Additionally, related methods typically fuse features at pixel-level and assume that all camera views are equally informative. In this work, we show that this approach leads to sub-optimal 3D features, both in terms of grounding accuracy, as well as segmentation crispness. To alleviate this, we propose a multi-view feature fusion strategy that employs object-centric priors to eliminate uninformative views based on semantic information, and fuse features at object-level via instance segmentation masks. To distill our object-centric 3D features, we generate a large-scale synthetic multi-view dataset of cluttered tabletop scenes, spawning 15k scenes from over 3300 unique object instances, which we make publicly available. We show that our method reconstructs 3D CLIP features with improved grounding capacity and spatial consistency, while doing so from single-view RGB-D, thus departing from the assumption of multiple camera views at test time. Finally, we show that our approach can generalize to novel tabletop domains and be re-purposed for 3D instance segmentation without fine-tuning, and demonstrate its utility for language-guided robotic grasping in clutter

Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models

Large Language Models (LLMs) have emerged as a new paradigm for embodied reasoning and control, most recently by generating robot policy code that utilizes a custom library of vision and control primitive skills. However, prior arts fix their skills library and steer the LLM with carefully hand-crafted prompt engineering, limiting the agent to a stationary range of addressable tasks. In this work, we introduce LRLL, an LLM-based lifelong learning agent that continuously grows the robot skill library to tackle manipulation tasks of ever-growing complexity. LRLL achieves this with four novel contributions: 1) a soft memory module that allows dynamic storage and retrieval of past experiences to serve as context, 2) a self-guided exploration policy that proposes new tasks in simulation, 3) a skill abstractor that distills recent experiences into new library skills, and 4) a lifelong learning algorithm for enabling human users to bootstrap new skills with minimal online interaction. LRLL continuously transfers knowledge from the memory to the library, building composable, general and interpretable policies, while bypassing gradient-based optimization, thus relieving the learner from catastrophic forgetting. Empirical evaluation in a simulated tabletop environment shows that LRLL outperforms end-to-end and vanilla LLM approaches in the lifelong setup while learning skills that are transferable to the real world. Project material will become available at the webpage https://gtziafas.github.io/LRLL_project.

Towards Open-World Grasping with Large Vision-Language Models

The ability to grasp objects in-the-wild from open-ended language instructions constitutes a fundamental challenge in robotics. An open-world grasping system should be able to combine high-level contextual with low-level physical-geometric reasoning in order to be applicable in arbitrary scenarios. Recent works exploit the web-scale knowledge inherent in large language models (LLMs) to plan and reason in robotic context, but rely on external vision and action models to ground such knowledge into the environment and parameterize actuation. This setup suffers from two major bottlenecks: a) the LLM's reasoning capacity is constrained by the quality of visual grounding, and b) LLMs do not contain low-level spatial understanding of the world, which is essential for grasping in contact-rich scenarios. In this work we demonstrate that modern vision-language models (VLMs) are capable of tackling such limitations, as they are implicitly grounded and can jointly reason about semantics and geometry. We propose OWG, an open-world grasping pipeline that combines VLMs with segmentation and grasp synthesis models to unlock grounded world understanding in three stages: open-ended referring segmentation, grounded grasp planning and grasp ranking via contact reasoning, all of which can be applied zero-shot via suitable visual prompting mechanisms. We conduct extensive evaluation in cluttered indoor scene datasets to showcase OWG's robustness in grounding from open-ended language, as well as open-world robotic grasping experiments in both simulation and hardware that demonstrate superior performance compared to previous supervised and zero-shot LLM-based methods.