arXiv Computer Vision
CLARO: Clutter Aware Robot Orchestrator for Open-Vocabulary Grasping using VLM Agents
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Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.
arXiv AI
Oct 27
AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation
AffordSim is a scalable data generator and benchmark that integrates open‑vocabulary 3D affordance prediction into simulation‑based trajectory generation for robotic manipulation. Given a natural‑language task description, it synthesizes task‑relevant scenes, queries affordances, grounds them on object surfaces, samples region‑conditioned grasps, and selects executable candidates with motion planning, while randomizing pose, texture, lighting, noise, and backgrounds for sim‑to‑real transfer. The benchmark includes 50 tasks, five robot embodiments, and over 500 objects, achieving 93% of the success rate of manual contact annotations on affordance‑critical tasks and 89% on hard composite tasks, with policies trained on AffordSim transferring zero‑shot to a real Franka FR3 with 24% average success.
arXiv AI
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RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?
RoboSPA is a large-scale robotic manipulation dataset and benchmark designed to evaluate Vision‑Language‑Action models on fine‑grained spatial reasoning and long‑horizon procedural planning. It contains 10 task categories, 56 base tasks, and 280 variants across five difficulty levels, with 527K trajectories collected from multiple embodiments and scenes. The benchmark introduces diagnostic metrics beyond binary success, revealing that current VLA models struggle with complex spatial relations, precise execution, and memory‑intensive planning.
arXiv AI
Oct 15
Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation
arXiv:2610.14106v1 Announce Type: cross Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically ground...
arXiv AI
Oct 28
RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding
RoboAuto is a framework that enhances autonomous robot program search by explicitly incorporating physical understanding at the object, scene, and interaction levels. It uses a hierarchical knowledge base of executable skills and object-centric affordances, scene grounding to map knowledge to the current environment, and multi-stage planning with self-designed experiments to verify physical effects and resolve uncertainties. The approach achieves higher success rates than prior methods on multiple benchmark tasks and demonstrates transferability across simulators and embodiments.
arXiv Machine Learning
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Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations
The paper introduces a continual‑learning framework for single‑view 6‑DoF grasp synthesis with a parallel‑jaw gripper in cluttered scenes. Instead of fine‑tuning a large parametric model, the method updates grasp scores via memory in a learned embedding space and optionally incorporates user demonstrations to generate new candidate grasps. Experiments in simulation and real‑world trials (over 1500 grasps) show that the approach matches baseline performance before adaptation, improves online on unseen objects, and achieves over 90% success on challenging categories after just 50 online attempts.