홈 › Papers

Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control

2025-09-07 · Jun Yamada, Adithyavairavan Murali, Ajay Mandlekar, Clemens Eppner, Ingmar Posner, Balakumar Sundaralingam arxiv

Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered environments. Grasp prediction errors and object pose changes during grasping are the main causes of failure. In contrast, closed-loop methods address these challenges in simplified settings (e.g., single object on a table) on a limited set of objects, with no path to generalization. We propose Grasp-MPC, a closed-loop 6-DoF vision-based grasping policy designed for robust and reactive grasping of novel objects in cluttered environments. Grasp-MPC incorporates a value function, trained on visual observations from a large-scale synthetic dataset of 2 million grasp trajectories that include successful and failed attempts. We deploy this learned value function in an MPC framework in combination with other cost terms that encourage collision avoidance and smooth execution. We evaluate Grasp-MPC on FetchBench and real-world settings across diverse environments. Grasp-MPC improves grasp success rates by up to 32.6% in simulation and 33.3% in real-world noisy conditions, outperforming open-loop, diffusion policy, transformer policy, and IQL approaches. Videos and more at http://grasp-mpc.github.io.

📄 PDF Abstract BibTeX arXiv:2509.06201

Code (0)

등록된 구현이 없습니다.

Tasks

Collision Avoidance

Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing

2020-01-16 · Jesse Haviland, Feras Dayoub, Peter Corke

This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasping controllers are not closed-loop and …

Object Robotic Grasping valid

Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations

2019-12-09 · Shuran Song, Andy Zeng, Johnny Lee, Thomas Funkhouser

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to the challenges of collecting effective …

Reinforcement Learning

AdaClearGrasp: Learning Adaptive Clearing for Zero-Shot Robust Dexterous Grasping in Densely Cluttered Environments

2026-03-11 · Zixuan Chen, Wenquan Zhang, Jing Fang, Ruiming Zeng 외 arxiv

In densely cluttered environments, physical interference, visual occlusions, and unstable contacts often cause direct dexterous grasping to fail, while aggressive singulation strategies may compromise safety. Enabling ro…

Zero-shot Generalization Reinforcement Learning

Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds

2020-10-02 · Lirui Wang, Yu Xiang, Wei Yang, Arsalan Mousavian 외

6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting, which are sensitive to g…

Imitation Learning Motion Planning Reinforcement Learning (RL)Robotic Grasping

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

2018-06-27 · Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz 외

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in rob…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)