PhD Position F/M Learning diffusion policies for robotic manipulation

Contract type : Fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

Context

As part of a partnership between Inria and the La Poste group, our team seeks to leverage generative artificial intelligence techniques to enable a robot to grasp and handle mailbags of varying sizes and weights. Mailbags combine several open challenges: they are soft (like clothes), do not have any defined shape, and are typically organized in a heap (compared to a stack of boxes).

We aim to build on diffusion techniques [1] that allow learning image-conditioned policies from demonstrations. These techniques reuse ideas developped for generating images (Dall-E, MidJourney, etc.) but to generate trajectories.

The objective of this thesis will be to (1) evaluate current techniques in this context and (2) propose improvements to diffusion techniques to make them more effective (learning with fewer examples, better generalization, taking into account the constraints of the task and the robot, etc.).

[1] Chi C, Feng S, Du Y, Xu Z, Cousineau E, Burchfiel B, Song S. Diffusion policy: Visuomotor policy learning via action diffusion. arXiv preprint arXiv:2303.04137. 2023. https://arxiv.org/abs/2303.04137 https://www.youtube.com/watch?v=w-CGSQAO5-Q

 

Assignment

This PhD will be mainly supervised by Jean-Baptiste Mouret and Serena Ivaldi. It will be focused on contributions in machine learning that can be used in robotics, which will be mainly published in the main conferences in robotics (ICRA/IROS/CoRL/RSS) and machine learning (NeurIPS, ICLR, ICML, ...).

The experiments will be carried on with the Tiago++ robot (PAL Robotics), with the help of the research engineers of the team.

The PhD will be synchronized with the other work of the team, which exploit similar techniques but for other applications.

Main activities

Main activities (5 maximum) :

  • read and understand the relevant scientific papers
  • propose new techniques and new algorithms in machine learning
  • implement these algorithms
  • evaluate these algorithms on experimental datasets
  • write scientific articles 
  • present the work in scientific conferences

 

Skills

Technical skills and level required:

  • very good knowledge of machine learning
  • very good level in python, especially with the current machine learning libraries (Pytorch / numpy / etc.)
  • knowledge of robotics is appreciated

Languages :

  • the main language of the team is English and all the communication will be in English
  • French is not required.

Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage

Remuneration

2100€ gross/month the 1st year