Robot manipulation of multiple objects is an important topic for applications including
warehouse automation, service robots performing cleaning, and large-scale object sorting …

We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex
interactions that ensue as a robot end-effector pushes multiple objects, whose physical …

This work explores the use of topological tools for achieving effective non-prehensile
manipulation in cluttered, constrained workspaces. In particular, it proposes the use of …

We propose a novel approach for automatically generating a move plan for scene
arrangement. 1 Given a scene like an apartment with many furniture objects, to transform its …

杂乱环境中机器人推动与抓取技能自主学习问题被学者广泛研究, 实现二者之间的协同是提升抓
取效率的关键, 本文提出一种基于生成对抗网络与模型泛化的深度强化学习算法GARL-DQN …

This paper introduces a new technique for learning probabilistic models of mass and friction
distributions of unknown objects, and performing robust sliding actions by using the learned …

This paper presents a motion planning algorithm that enables robots to efficiently pick up
objects by considering simultaneous multi-object grasping. At the center of the algorithm is a …

Arranging objects from a random and scattered distribution into an integral part has many
applications, including bin packing, logistics, and other industrial fields. Measurement …

This paper proposes a combined planning and optimization method that enables a dual-arm
robot to lift up and flip heavy plates using crane pulley blocks. The problem is motivated by …

Object rearrangement is widely demanded in many of the manipulation tasks performed by
industrial and service robots. Rearranging an object through planar pushing is deemed …