Andreas Christou, Andreas Sochopoulos, Elliot Lister and Sethu Vijayakumar, Human-in-the-Loop Optimisation in Robot-Assisted Gait Training, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025), Hangzhou, China (2025). [pdf] [video] [citation]
Wearable robots offer a promising solution for quantitatively monitoring gait and providing systematic, adaptive assistance to promote patient independence and improve gait. However, due to significant interpersonal and intrapersonal variability in walking patterns, it is important to design robot controllers that can adapt to the unique characteristics of each individual. This paper investigates the potential of human-in-the-loop optimisation (HILO) to deliver personalised assistance in gait training. The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) was employed to continuously optimise an assist-as-needed controller of a lower-limb exoskeleton. Six healthy individuals participated over a two-day experiment. Our results suggest that while the CMA-ES appears to converge to a unique set of stiffnesses for each individual, no measurable impact on the subjects' performance was observed during the validation trials. These findings highlight the impact of human-robot co-adaptation and human behaviour variability, whose effect may be greater than potential benefits of personalising rule-based assistive controllers. Our work contributes to understanding the limitations of current personalisation approaches in exoskeleton-assisted gait rehabilitation and identifies key challenges for effective implementation of human-in-the-loop optimisation in this domain.
Nils Dengler, Juan Del Aguila Ferrandis, Joao Moura, Sethu Vijayakumar and Maren Bennewitz, Learning Goal-Directed Object Pushing in Cluttered Scenes With Location-Based Attention, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025), Hangzhou, China (2025). [pdf] [video] [citation]
In complex scenarios where typical pick-and-place techniques are insufficient, often non-prehensile manipulation can ensure that a robot is able to fulfill its task. However, nonprehensile manipulation is challenging due to its underactuated nature with hybrid-dynamics, where a robot needs to reason about an object’s long-term behavior and contact-switching, while being robust to contact uncertainty. The presence of clutter in the workspace further complicates this task, introducing the need to include more advanced spatial analysis to avoid unwanted collisions. Building upon prior work on reinforcement learning with multimodal categorical exploration for planar pushing, we propose to incorporate location-based attention to enable robust manipulation in cluttered scenes. Unlike previous approaches addressing this obstacle avoiding pushing task, our framework requires no predefined global paths and considers the desired target orientation of the manipulated object. Experimental results in simulation as well as with a real KUKA iiwa robot arm demonstrate that our learned policy manipulates objects successfully while avoiding collisions through complex obstacle configurations, including dynamic obstacles, to reach the desired target pose.