[meetings][CfP] Deadline Extended to August 18th: IEEE/RSJ IROS 2023 Workshop “Learning Meets Model-based Methods for Manipulation and Grasping”

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Raffaello Camoriano

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Jul 18, 2023, 4:40:48 AM7/18/23
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Dear colleagues,

Based on multiple requests, the paper submission deadline for the IROS 2023 Workshop “Learning Meets Model-based Methods for Manipulation and Grasping” has been extended to August 18th, 2023 (11.59PM, AoE) to facilitate participation.

The workshop's objective is to provide the robotics community with a comprehensive and up-to-date overview of methods for robotic manipulation and grasping across the spectrum of model-based and learning-based approaches. An exciting lineup of speakers will provide attendees with a unique view on the field.

We solicit the submission of short papers (maximum 4 pages) covering recent or ongoing work and early results on the workshop's themes, to be presented during an interactive poster session. One (or more) of the accepted contributions will be selected for a best paper and/or best student paper award. Financial support for workshop paper authors from underrepresented groups will be announced after decision notification.

Learn more: https://sites.google.com/view/learning-meets-models-iros2023/call-for-papers

Submit your work: https://cmt3.research.microsoft.com/IROSwkshpL4MG2023/Submission/Index

We are looking forward to meeting you in Detroit.

Important Dates

  • Submission deadline: July 17th August 18th, 2023 (11.59PM, anywhere on Earth)
  • Notification of publication decision: September 10th, 2023
  • Final version due: September 18th, 2023
  • Workshop: October 5th, 2023 (full day)

Abstract

Building robots capable of dexterous interaction with objects to carry out fine manipulation tasks has always been a grand challenge in robotics. The non-smooth, brittle nature of manipulator-object mechanics, together with perceptual uncertainty, easily violate the assumptions of early planning and control methods. Furthermore, accurate physical modeling of complex or non-rigid mechanical systems requires large amounts of computations, which is incompatible with real-time control.

Such challenges led researchers to develop a wide range of approaches, from adaptive control tailored to the (potentially changing) properties of the object at hand, to advanced perception to tackle measurement uncertainty. Machine learning also contributed by providing actionable representations of complex geometries and visual appearance, and by encoding hard-to-model expert demonstrations to reduce the cost of trial-and-error. In turn, these informed the development of novel robot control methods enabling more robust and dexterous skills. At the same time, the employment of mechanical models proved effective for enforcing structural constraints in robot control systems (including learning-based ones), thus improving safety and guiding exploration.

However, there are still many open challenges that need to be addressed to achieve long-horizon robotic manipulation and sidestep the computational burden of accurate simulation of contact-rich scenarios. The ambition of this workshop is to provide a comprehensive overview of the broad and scattered state of the art in robot manipulation and grasping, spanning model-based and learning-based approaches. Talks and interactive sessions will enable a deeper understanding of current approaches in different use cases, while stimulating the development of new methods.

Workshop Topics

We solicit the submission of short papers covering recent work, early results, or ongoing work on the workshop's themes.

Research topics include, but are not limited to:

  • Model-based Methods for Manipulation and Grasping
  • Adaptive Control for Manipulation and Grasping
  • Model Learning
  • (Deep) Reinforcement Learning
  • Imitation Learning
  • In-hand Manipulation
  • Model Predictive Control for Manipulation and Grasping
  • Learning Manipulation and Grasping on Robot Constraint Manifolds
  • Structured Learning for Robotics
  • Vision-based Learning for Grasping and Manipulation
  • Multimodal learning for Grasping and Manipulation
  • Learning-based and Model-based methods for prosthetic robotic grasping and manipulation
  • Synergy based grasp planning
  • Human-inspired grasp planning
  • ...

Submission Instructions

Short paper submission format:

Accepted papers will be presented in-person during an interactive poster session. At least one of the authors needs to be physically present at the venue for each poster. Authors can opt-in to make accepted papers downloadable from the workshop website after IROS.

For any questions, feel free to contact us through the form available on the website: https://sites.google.com/view/learning-meets-models-iros2023/contacts

Invited Speakers

Organizing Committee

Institutional Partners

We would like to thank our institutional partners for their invaluable support:
Best regards,
Raffaello Camoriano, on behalf of the organizing committee.

--
Raffaello Camoriano
Assistant Professor (non-TT, RTDa) in Machine Learning and Robotics
Visual and Multimodal Applied Learning Laboratory (VANDAL), 
ELLIS Unit Turin
DAUIN, Politecnico di Torino, Turin, Italy

Skype ID: raffaello.camoriano
Mailing: C.so Francesco Ferrucci, 112 - 10141 Turin, Italy
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