Abstract: Reinforcement learning and model-based control are complementary approaches for soft and continuum robots. While RL has recently been very successfully applied to robotics, soft robots still pose unique problems: unconventional robot designs, rich contact interactions, underactuation, complex state representations, heavy simulation costs, and challenging sim-to-real transfer. Reinforcement Learning can be used to complement traditional control and design strategies and cover some of their limitations. In this talk, we will present SofaGym, a collection of Gym-style environments for training RL controllers on soft-robots, to discuss the specific challenges of soft robotics and how RL may help address them. We will then focus on an interventional cardiology example to show how Monte Carlo Tree Search can be used to explore nonlocal strategies and complement optimization-based inverse-model control. We will also present work on object manipulation using deformable grippers that combine hybrid approaches involving model-based control and learning. We will conclude by noting that observing nature leads us to consider that intelligence is broader than control, and in particular to develop methods for enhancing the robot’s physical—mechanical—intelligence, which simplifies control.

Bio: Christian Duriez is a research director at Inria Lille, France, where he leads the DEFROST team. He earned his Ph.D. in Robotics from the CEA and Université d’Évry in 2004, followed by a postdoctoral position at CIMIT SimGroup in Boston, USA. His research focuses on interactive simulation of deformable objects, haptic rendering, and surgical simulation, with a strong emphasis on soft robotics modeling and control, fast finite element methods, and contact response simulation. A key contributor to the open-source SOFA framework, he co-founded InSimo (2013) and Compliance Robotics (2024), where he advances the use of soft robots for educational and industrial applications, particularly in the food industry.