2nd Workshop on Dexterous Manipulation:
Design, Perception and Control

Full-Day RSS 2024 Workshop, July 15, 2024
Live Stream on YouTube: Morning Session, Afternoon Session

Introduction

This workshop aims to pioneer the exploration of learning-based strategies for achieving dexterous manipulation in robotics. Dexterous manipulation is among the most challenging problems in robotics, and this event seeks to provide insights and viewpoints for researchers and attendees on this subject. We will delve into current research and advancements in the hardware design of dexterous manipulators, generalizable manipulation learning approaches, and sensing technologies like tactile sensors.

Following our first dexterous manipulation workshop in RSS 2023. This workshop is Intended for a wide range of audiences, including researchers, engineers, and students with a strong foundation or interests in machine learning, computer vision, robotics, and related fields. The workshop promises a rich dialogue among leading experts from academic and industrial backgrounds. Accepted papers will have a chance to be presented in poster sessions and contributed talks. Through these events, participants will gain insights into the latest breakthroughs and collaborative opportunities, setting the stage for future innovations in robotic learning.

Invited Speakers

(listed alphabetically)


Workshop Schedule

Time (GMT+1) Event
08:45 - 08:50 Introduction and Opening Remark
08:50 - 09:15 Invited Talk (Sammy Christen): Physically Plausible and Natural Synthesis of Hand-Object Interactions
09:15 - 09:40 Invited Talk (Hanbyul Joo): Towards capturing everyday movements to scale up and enrich human motion data
09:40 - 10:10 Spotlight Talks I
10:10 - 10:30 Coffee Break + Poster Session + Real Robot Demo Session
10:30 - 10:55 Poster Session + Real Robot Demo Session
10:55 - 11:20 Invited Talk (He Wang): Learning Generalizable Dexterous Grasping in Cluttered Environment via Sim2Real
11:20 - 11:45 Invited Talk (Robert Katzschmann): Giving Robots a Hand
11:45 - 12:10 Invited Talk (Kevin Doherty): KFC Fusion: Cooking up Kinematics, Forces, and Camera Data for Object State Estimation
12:10 - 14:00 Lunch Break
14:00 - 14:25 Invited Talk (Matei Ciocarlie): Aiming for Dexterity: A New Hand Design with a New F/T Sensor
14:25 - 14:50 Invited Talk (Tess Hellebrekers): Magnetic tactile sensing for robotic dexterity
14:50 - 15:15 Invited Talk (Jitendra Malik): Learning Approaches to Dexterous Manipulation
15:15 - 15:35 Spotlight Talks II
15:35 - 16:00 Coffee Break + Poster Session + Real Robot Demo Session
16:00 - 17:00 Panel Discussion
17:00 - 17:15 Closing Remark & Award Ceremony

Call for Papers/Demos

In this workshop, our goal is to bring together researchers from various fields of robotics, such as control, optimization, learning, planning, sensing, hardware, etc., who work on dexterous manipulation.

We are particularly excited to offer a platform for showcasing real-world robotic systems. Even without a formal paper, we encourage submissions of videos demonstrating your robots in action. For those able to attend in person, there will be opportunities to showcase your robots live at the workshop! We encourage researchers to submit work in the following areas (the list is not exhaustive):

Submission Guidelines

Timeline


Poster Sessions

Spotlight Session I (09:40-10:10) Spotlight Session II (15:15-15:35)
Paper Name Authors Paper Name Authors
3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, Huazhe Xu SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty Sirui Chen, Jeannette Bohg, Karen Liu
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation Chen Wang, Haochen Shi, Weizhuo Wang, Ruohan Zhang, Li Fei-Fei, Karen Liu Tilde: Teleoperation for Dexterous In-Hand Manipulation Learning with a DeltaHand Zilin Si, Kevin Lee Zhang, Zeynep Temel, Oliver Kroemer
ContactMPC: Towards Online Adaptive Control for Contact-Rich Dexterous Manipulation Arjun Lakshmipathy, Nancy S. Pollard DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes Jialiang Zhang, Haoran Liu, Danshi Li, Xinqiang Yu, Haoran Geng, Yufei Ding, Jiayi Chen, He Wang
Tactile Exploration with Particle-Based Belief Entropy Lara Bruderm├╝ller, Julius Jankowski, Sylvain Calinon, Marc Toussaint, Nick Hawes Next-Gen Manipulation: An Active Surface-Based Underactuated Gripper with Visual Feedback Karthik Swaminathan, Saad Hashmi, Alqama Shaikh, Vikas Phalle
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands Le Chen, Yi Zhao, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Sch├Âlkopf, Joni Pajarinen, Dieter B├╝chler PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations Cheng Qian, Julen Urain, Kevin Zakka, Jan Peters
LEAP Hand V2: Dexterous, Low-cost Anthropomorphic Hybrid Rigid Soft Hand for Robot Learning Kenneth Shaw, Deepak Pathak Seahorse Tail-inspired Soft Pneumatic Actuator Utilizing Dual-mode Actuation Dickson Chiu Yu Wong, Zijian Zhou, Garmisch Lai Yin Wong, Rob B.N. Scharff
OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris M. Kitani, Changliu Liu, Guanya Shi A Dexterous Robotic Hand for In-Hand Manipulation of Long, Thin Objects Abdullah Nazir
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation Hui Zhang, Sammy Christen, Zicong Fan, Luocheng Zheng, Jemin Hwangbo, Jie Song, Otmar Hilliges CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz, Akhilan Gurumoorthy, Hao Su, Xiaolong Wang
A Compliant Gripper System for Delicate Object Grasping through Intrinsic Contact Sensing Tao Yu, Jiepeng Wang, Shujie Tang, Xingyue Zhu, Genliang Chen
Sarcomere Dynamics - High Dexterity Robotic Hands Harpal Mandaher

Organizers

Contact

For questions and comments, please contact us.
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