Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
We apply our system to perform articulated object manipulation, both in the simulation and the real world.
In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably.
One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function.
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
The ability to perform in-hand manipulation still remains an unsolved problem; having this capability would allow robots to perform sophisticated tasks requiring repositioning and reorienting of grasped objects.
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
Our model jointly estimates (i) the segmentation of the scene into an unknown but finite number of objects, (ii) the motion trajectories of these objects and (iii) the object scene flow.
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.